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Supervision

Supervision is the act of overseeing and directing the work of others to ensure tasks are completed effectively and efficiently.
It involves providing guidance, feedback, and support to subordinates or team members to help them achieve their goals and maintain high levels of performance.
Supervisors are responsible for monitoring progress, identifying and addressing issues, and making decisions to optimize workflow and outcomes.
Effective supervision requires strong communication, problem-solving, and leadership skills, as well as the ability to motivate and develop employees.
Typo: 'peformance' instead of 'performance'.

Most cited protocols related to «Supervision»

Multilabel Learning of Signal Peptide Regions

Cited 89 times

We defined the task of learning SP regions as a multilabel classification problem at each sequence position. Multilabel differs from multiclass in the sense that more than one label can be true at a given position. This approach was motivated by the fact that there is no strict definition of region borders that is commonly agreed upon, making it impossible to establish ground-truth region labels for models to train on. We thus used the multilabel framework as a method for training with weak supervision, allowing us to use overlapping region labels during the learning phase that could be generated from the sequence data using rules. For inference, we did not make use of the multilabel framework, as we only predicted the single most probable label at each position using Viterbi decoding, yielding a single unambiguous solution.
We defined a set of three rules based on known properties of the n-, h-, and c-regions. The initial n-region must have a minimum length of two residues and the terminal c-region a minimum length of three residues. The most hydrophobic position, which is identified by sliding a seven-amino-acid window across the SP and computing the hydrophobicity using the Kyte–Doolittle scale^{29 (link)}, belongs to the h-region. All positions between these six labeled positions are labeled as either both n and h or h and c, yielding multitag labels.
This procedure was adapted for different SP classes, with only Sec/SPI completely following it. For Tat SPs, the n–h border was identified using the twin-arginine motif. All positions before the motif were labeled n, followed by two dedicated labels for the motif, again followed by a single position labeled n. For SPII SPs, we did not label a c-region, as the C-terminal positions cannot be considered as such^{30 (link)}. The last three positions were labeled as the lipobox, all positions before that as h only. For SPIII SPs, no region labels were generated within the SP.

Teufel F., Almagro Armenteros J.J., Johansen A.R., Gíslason M.H., Pihl S.I., Tsirigos K.D., Winther O., Brunak S., von Heijne G, & Nielsen H. (2022). SignalP 6.0 predicts all five types of signal peptides using protein language models. Nature Biotechnology, 40(7), 1023-1025.

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Publication 2022

Amino Acids Arginine Debility Supervision Twins

Prostate Cancer Registry: CaPSURE Cohort

Cited 49 times

The Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE^™) is a national disease registry accruing men with biopsy-proven prostate adenocarcinoma, recruited from 40 urology practices, primarily community-based, across the United States. Informed consent is obtained from each patient under institutional review board supervision. Patients are treated according to their physicians’ usual practices, and are followed until time of death or withdrawal from the study. Additional details have been reported previously.^{13 (link), 14 (link)} Eligibility for inclusion in the study was limited to men with prostate cancer diagnosed since 1992 who underwent prostatectomy as primary treatment and had at least six months of followup recorded in the registry. Those with clinically advanced disease (>cT3aN0M0) pre-operatively were ineligible, as were those had received neoadjuvant or adjuvant hormonal and/or radiation.
Detailed reporting of staging variables (ECE, SVI, SM) is variable among pathology reports accessioned to CaPSURE. In the main analysis, ECE, SVI, or SM reported as “unable to assess” were assumed to be negative; in a sensitivity analysis, cases without complete data for all variables were dropped. To examine whether cases with missing pathologic data (ECE, SVI, SM) differed from cases with complete data, we compared these groups with respect to their distributions of the original preoperative CAPRA score using a Wilcoxon rank-sum statistic. In all cases, patients with no lymphadenectomy performed were assumed to have negative LNI. Patients missing pathologic Gleason score and/or preoperative PSA were excluded.
The definition of biochemical recurrence was either 2 consecutive PSA values over 0.2 ng/ml^{15 (link)} or any secondary treatment at least six months following surgery (treatment within six months was assumed to be adjuvant). Men not experiencing recurrence—including those dying of other causes—were censored at date of the last available PSA.

Cooperberg M.R., Hilton J.F, & Carroll P.R. (2011). The CAPRA-S score: a straightforward tool for improved prediction of outcomes after radical prostatectomy. Cancer, 117(22), 5039-5046.

Publication 2011

Adenocarcinoma Biopsy Eligibility Determination Ethics Committees, Research Goat Hypersensitivity Lymph Node Excision Neoadjuvant Therapy Operative Surgical Procedures Patients Pharmaceutical Adjuvants Physicians Prostate Cancer Prostatectomy Prostatic Diseases Radiotherapy Recurrence Supervision

Florbetapir-PET Imaging for Amyloid Detection

Cited 48 times

Participants were imaged at 23 sites using clinical PET and PET/computed tomographic scanners. Each participant underwent a 10-minute PET scan, which began 50 minutes after receiving an intravenous bolus of 370 MBq (10 mCi) florbetapir F 18. Images were acquired with a 128 × 128 matrix (zoom × 2) and were reconstructed using iterative or row action maximization likelihood algorithms.
Florbetapir-PET images were assessed visually using a semiquantitative score ranging from 0 (no amyloid) to 4 (high levels of cortical amyloid) by 3 board-certified nuclear medicine physicians who were not involved in any other aspects of the study. The only experience these physicians had with florbetapir-PET imaging occurred during a half-day training session. The median rating of the readers served as a primary outcome variable. Readers were blinded to clinical, demographic, and neuropathological information and viewed and rated images under the supervision and at the facility of the imaging core laboratory (ImageMetrix, a division of the American College of Radiology, Philadelphia, Pennsylvania). The initial 6 postmortem evaluations were rated by 4 readers and the median rating of the 4 raters served as the primary outcome variable for these 6 participants.
For the younger control cohort, the PET images were mixed in random order with 40 images from the autopsy cohort that had a median visual read score between 2 and 4 (inclusive). To remove image recognition bias, these images were rated as amyloid positive or negative at ImageMetrix by a different group of 3 external readers. The majority rating was used as the primary outcome variable for this analysis.
A semiautomated quantitative analysis of the ratio of cortical to cerebellar signal (SUVr) also was performed for florbetapir-PET images from all study participants. The images were first normalized to a standard template in the Talairach space and then the SUVrs were calculated for the 6 predefined cortical regions of interest (frontal, temporal, parietal, anterior cingulate, posterior cingulate, and precuneus). The whole cerebellum was used as the reference region.

Clark C.M., Schneider J.A., Bedell B.J., Beach T.G., Bilker W.B., Mintun M.A., Pontecorvo M.J., Hefti F., Carpenter A.P., Flitter M.L., Krautkramer M.J., Kung H.F., Coleman R.E., Doraiswamy P.M., Fleisher A.S., Sabbagh M.N., Sadowsky C.H., Reiman E.M., Zehntner S.P, & Skovronsky D.M. (2011). Use of Florbetapir-PET for Imaging β-Amyloid Pathology. JAMA, 305(3), 275-283.

Publication 2011

Amyloid Proteins Autopsy CAT SCANNERS X RAY Cerebellum Cortex, Cerebral florbetapir florbetapir F 18 Gyrus, Anterior Cingulate Inclusion Bodies Physicians Posterior Cingulate Cortex Precuneus Radiography Radionuclide Imaging Supervision

Feasibility of Cognitive-Behavioral Swallowing Therapy

Cited 47 times

As this was primarily a feasibility study, primary outcomes were those that related to the acceptability of the intervention to participants and the feasibility of trialling the intervention in a larger study. The acceptability and feasibility outcomes are as follows:

Acceptability was measured by the proportion of patients approached and consented and the number of sessions attended. Retention rates and reasons for drop out was documented. We aimed for a 50% recruitment rate for CB-EST to be deemed an acceptable treatment.

Feasibility and fidelity were measured by assessing whether the intervention could be delivered as planned, by a SLT with CBT training. Session content and treatment plans, recorded in patients’ notes were evaluated by a CBT expert practitioner as part of supervision, reliability and validity checking. Content analysis of sessions including a) whether a therapy goal was identified b) whether a CBT formulation was identified c) whether cognitive and/or behaviour change techniques were used. These outcomes would also indicate the acceptability of the intervention to patients.

A selection of candidate measures targeting swallowing self-report, dietary restrictions, quality of life, functioning and mood were chosen to identify appropriate tools to capture CB-EST outcomes. Acceptability to patients was monitored by percentage data completion. The measures listed below and were administered pre-, immediately following CB-EST, and at three months.

The MDADI [13 ] has twenty items, each marked using a five-point scale and summarised using a total score (range 20–100). Higher scores indicate a better outcome and a change in ≥10 points is considered a clinically significant difference [15 ].

ii.

The European Organization for Research and Treatment of Cancer questionnaires (EORTC QLQ-C30) [16 (link)] is a general quality of life questionnaire with 30 items, five functioning scales (physical, role, emotional, cognitive, and social), three symptom scales. The EORTC QLQ-H&N35 is a disease-specific module of 35 questions divided into 7 subscales about pain, swallowing, senses, speech, social eating, social contact, and sexuality. Higher scores on the functional scales refer to better health status, whereas higher scores in symptom scales and the QLQ-H&N35 represent more severe symptoms.

iii.

Chalder Fatigue Questionnaire (CFQ-11) [17 (link)] measures fatigue severity. Eleven items are answered on a four-point scale (range 0–33), with high scores representing more fatigue.

iv.

Work and Social Adjustment Scale(WASA) [18 (link)] measures functional and social impairment. Five questions are answered on a nine-point scale (range 0–40) with higher scores indicating more impairment.

Hospital Anxiety and Depression Scale (HADS) [19 (link)] has two seven item subscales measuring anxiety (HADS-A) and depression (HADS-D). Each item is scored on a four-point scale (range 0–21 for each subscale). Subscale scores 0–7 classify participants as non-cases, 8–10 indicates borderline cases, and scores ≥11 indicate clinical levels. Total HADS scores (HADS-T) ≥ 15 indicate clinically significant distress.

vi.

Performance Status Scales (PSS) Normalcy of Diet [20 (link)] measures diet texture restrictions and is clinician-rated. The scale has ten ranked categories ranging from 0 (nil by mouth) to 100 (full diet without restrictions).

The presence of a feeding tube was recorded at the same time points. The sensitivity of the candidate measures was tested by making preliminary estimates of change from pre- to post CB-EST. Data were analysed using SPSS v21 (Chicago, Illinois). We used a one way within subjects repeated measures analysis of variance complete case model. The level for statistical significance was set at 0.05. Bonferroni’s test was used for multiple post hoc comparisons. Means are reported with standard deviations and 95% confidence intervals.

The acceptability and feasibility of delivering CB-EST as-was or modifying it for a larger trial was further assessed using semi-structured interviews. Patients were purposively sampled to ensure a range of pre to post CB-EST changes in MDADI scores, a range of HNSCC treatment and time post-treatment. Patients were selected from those at the initial stages of CB-EST and at the end of CB-EST. Interviews were conducted by two independent researchers. Patients had the option of a telephone or face to face interview, at a time and place of their choice. All interviews were digitally recorded, transcribed verbatim and anonymised. Transcripts were read several times and in detail by the qualitative sub-team. Data were then discussed and coded using thematic analysis. Quotations relating to afore mentioned topics were independently selected and coded into key issues and themes.

Patterson J.M., Fay M., Exley C., McColl E., Breckons M, & Deary V. (2018). Feasibility and acceptability of combining cognitive behavioural therapy techniques with swallowing therapy in head and neck cancer dysphagia. BMC Cancer, 18, 1.

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Publication 2018

Anxiety Behavior Therapy Cognition Diet Dietary Restriction Emotions Europeans Face Fatigue Hypersensitivity Infantile Neuroaxonal Dystrophy Malignant Neoplasms Mood Oral Cavity Pain Patients Physical Examination Retention (Psychology) Speech Squamous Cell Carcinoma of the Head and Neck Supervision Tube Feeding

Benchmarking Trajectory-based Differential Expression

Cited 39 times

The simulation study evaluates methods that (differentially) associate gene expression with pseudotime for three different trajectory topologies, i.e., a cyclic, a bifurcating, and a multifurcating trajectory. As independent evaluation, we use the extensive trajectory simulation framework dynverse that previously served for benchmarking trajectory inference methods in Saelens et al.^{1 (link)}. Interested readers should refer to the original publication for details on the data simulation procedure. Data set characteristics are listed in Table 1.Table 1

Overview of simulated data sets.

	Cyclic data set	Bifurcating data set	Multifurcating data set
Simulation framework	dyngen	dyntoy	dyntoy
Number of cells	505–508	500	750
Number of genes	312–444	5000	5000
% of DE genes	42–47%	20%	20%
Number of lineages	1	2	3
Topology	Cyclic	Bifurcating	Multifurcating
Number of data sets	10	10	1

Each data set is simulated using one of the frameworks from the dynverse toolbox (dyngen or dyntoy), which are designed to simulate scRNA-seq data according to trajectory topologies. Each data set can be characterized by the topology of the trajectory, as well as the number of cells and genes. Low-dimensional representations of representative data sets can be found in Fig. 3. Note that the cyclic data sets have some variation in the numbers of genes and cells and in the amount of differential expression, which is inherent to the dyngen simulation framework.

For each of the cyclic and bifurcating topologies, we generate and analyze ten data sets. Since the multifurcating topology is very variable across simulations due to its flexible definition, its analysis requires substantial supervision. Therefore, we analyze only one representative multifurcating data set.
Prior to trajectory inference, the simulated counts are normalized using full-quantile normalization^{35 (link),36 (link)}. For TI with slingshot, we apply principal component analysis (PCA) dimensionality reduction to the normalized counts and k-means clustering in PCA space. For the bifurcating and multifurcating trajectories, the start and end clusters of the true trajectory are provided to slingshot to aid it in inferring the trajectory. For the edgeR analysis, we assess DE between the end clusters that are also provided to slingshot. The BEAM method can only test one bifurcation point at a time. For the multifurcating data set, we therefore assessed both branching points separately and aggregated the p-values using Fisher’s method³⁷. For the tradeSeq and edgeR analyses of the multifurcating data set, we perform global tests across all three lineages.
We assess performance based on scatterplots of the true positive rate (TPR) vs. the false discovery proportion (FDP), according to the following definitions

FDP = \frac{F P}{\max (1, F P + T P)} TPR = \frac{T P}{T P + F N},

where FN, FP, and TP denote, respectively, the numbers of false negatives, false positives, and true positives. FDP-TPR curves are calculated and plotted with the Bioconductor R package iCOBRA^{38 (link)}.

Van den Berge K., Roux de Bézieux H., Street K., Saelens W., Cannoodt R., Saeys Y., Dudoit S, & Clement L. (2020). Trajectory-based differential expression analysis for single-cell sequencing data. Nature Communications, 11, 1201.

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Publication 2020

Cells Gene Expression Genes Genetic Diversity Single-Cell RNA-Seq Supervision TpTp

Most recents protocols related to «Supervision»

Engaging Parents of Children with Medical Complexity

PCs of children with medical complexity were recruited from Complex Care Programs at SickKids, RVH, and CVH. To be eligible for the Complex Care Program, children must meet at least 1 criterion from each of the following conditions: technology dependence and/or users of high-intensity care (eg, mechanical ventilator, constant medical/nursing supervision), fragility (eg, severe/life-threatening condition, an intercurrent illness causing immediate serious health risk), chronicity (condition expected to last at least 6 more months or life expectancy less than 6 months), and complexity (involvement of at least 5 health care practitioners/teams at 3 different locations or family circumstances that impede their ability to provide day-to-day care of decision-making for a child with medical complexity) [18 ]. Children with medical complexity were also between 0 and 18 years of age at the time of study initiation. Purposive sampling guided parental participant selection to ensure diversity in role, communication experience, age, ethnicity, and location [19 (link),20 (link)].
PCs were eligible to participate if they were English-speaking, had access to the internet and a computer, and were the primary caregiver of a child with medical complexity. CTMs were approached prior to recruitment to ensure it was an appropriate time to engage in research for the families (eg, hospitalization, end-of-life, or PC physical/mental health concerns).
In this study, “NPs” refers to the nurse practitioners of children with medical complexity in the Complex Care Program, and “HCPs” refers to other hospital and community–based health care providers. CTMs comprise both NPs and HCPs together.
Every PC had their assigned Complex Care Program NP on the platform. PCs were also able to invite other members of their child’s care team (eg, CTMs like social workers, patient information coordinators, pediatricians, etc) to use C2. CTMs that registered on C2 were presented with the terms of use of the platform and the study information letter. If interested, they were approached by the study research coordinator (RC) and presented with information about the research study and the opportunity to participate. CTMs that declined to participate in the research study were still able to use C2. PCs and NPs received training before registering on C2 (duration of 30 to 60 minutes), and the training presentation was later made available on C2. In addition, CTMs could set up a disclaimer on C2 if they were away or designate time slots in which they would respond to messages (eg, 8 AM to 4 PM) to aid in setting expectations with PCs.
All research study participants received remuneration for participating in the research study. PCs were given CAD $60 (US $44.59) in gift cards (CAD $20 at baseline and CAD $40 after completing the study), and HCPs that completed the end-of-study questionnaire were entered into a draw for a CAD $100 (US $74.32) gift card. Participants that completed the end-of-study semistructured interview received an additional gift card worth CAD $20 (US $14.86). C2 also had a built-in points system where PCs received a specified number of points when completing a platform activity (ie, accessing educational material). As a usage incentive, PCs received a gift card worth CAD $5 (US $3.72) when they reached predetermined point milestones. NPs also received a CAD $5 (US $3.72) gift card for every 50 messages that they sent through C2.

Parpia C., Moore C., Beatty M., Miranda S., Adams S., Stinson J., Desai A., Bartlett L., Culbert E., Cohen E, & Orkin J. (2023). Evaluation of a Secure Messaging System in the Care of Children With Medical Complexity: Mixed Methods Study. JMIR Formative Research, 7, e42881.

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Publication 2023

Child cyclohexane-1,2,4-tris(methylenesulfonate) Day Care, Medical Ethnicity Hospitalization Intensive Care Mechanical Ventilator Medical Care Team Mental Health Parent Patients Pediatricians Physical Examination Practitioner, Nurse Supervision

Qualitative Insights on COVID-19 Curriculum Dissemination

For in-depth interviews, the research team selected a purposive sample of learners (N=12) across health professions from different regions globally. Because the course series was designed for community-based health workers, sampling focused on identifying health professionals with experience using and sharing course content as part of community-based pandemic responses.
The study considered the following learners for recruitment: (1) learners who indicated that they had shared course content with others in their network in a voluntary follow-up course satisfaction survey (administered by the consortium in December 2020; N=112); (2) learners in a community-based health worker role or in a supervisory role in a position to share information with community-based health workers (ie, doctors, nurses, health worker trainers/supervisors, and technical assistance providers) and at an organization with more than one enrollee in the course series; and (3) learners holding an educator role at a higher education institution with more than one enrollee in the course series. Learners who indicated that they did not consent to be contacted further in the follow-up course satisfaction survey were excluded from recruitment.
A total of 119 learners met the purposive sampling criteria and were recruited to participate via email in the study. Learners were sent an introductory recruitment email, and those who did not respond to the initial email were sent several follow-up email requests. Fourteen learners responded with willingness to participate in an in-depth interview (11.8% response rate). The research team was able to schedule in-depth interviews with a sample of 12 of these learners and made efforts to ensure representation across geographic regions and from LMICs. No additional recruitment was deemed necessary as the research team determined saturation was achieved.
As illustrated in Table 1, the 12 interview participants represented a diversity of geographic regions, with 42% (5/12) from Sub-Saharan Africa, followed by 25% (3/12) from North America and 25% (3/12) from South/Southeast Asia. Half (6/12, 50%) of the interviewed learners identified as female. The majority of interviewed learners (7/12, 58%) indicated affiliation with nongovernmental organizations (NGOs). The remaining interviewed learners held roles in governments (2/12, 17%), academic institutions (2/12, 17%), or intergovernmental organizations (1/12, 8%). Interviewed learners were doctors (3/12, 25%), health worker trainers or supervisors (3/12, 25%), community-based health workers (2/12, 17%), technical assistance providers (2/12, 17%), or educators (2/12, 17%). All were involved in community-based COVID-19 response activities, with nearly all (10/12, 83%) involved in risk communication and community engagement.
The 12 in-depth interviews were conducted one-on-one in English via videoconference by 2 investigators (NAS and NJ) using a semistructured interview guide. The interviewers asked learners about their experiences with the curriculum and their roles in using, adapting, and disseminating the curriculum. The interviews were audio recorded and transcribed. Interviews lasted between 20 and 57 minutes, with a mean duration of 38 minutes.

Skinner N.A., Job N., Krause J., Frankel A., Ward V, & Johnston J.S. (2023). The Use of Open-Source Online Course Content for Training in Public Health Emergencies: Mixed Methods Case Study of a COVID-19 Course Series for Health Professionals. JMIR Medical Education, 9, e42412.

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Publication 2023

ARID1A protein, human Community Health Workers COVID 19 Females Health Personnel Interviewers Nurses Pandemics Physicians Satisfaction Supervision Workers

Computer-Based Cognitive Bias Modification Intervention in Schools

Participant recruitment and baseline collection in Xinyang started in March 2021, and in Jiaxing in May 2021. This intervention study took place in the students’ school during their regular school hours over the course of about 4 weeks.
Participants who met inclusion criteria were gathered in their school computer labs before training and were asked to complete the computerized baseline assessments including WSAP-H, CATQ, and RPSQ. Then they were randomized to receive either CBM-I training or PCT training via an online random sequence generator from www.randomizer.org. The experimenters who randomly grouped the participants did not take part in the subsequent intervention implementation and data collection. Over the course of the following training month (4 weeks), participants received their assigned computer program trainings in the school computer labs individually once a week. After each training, students could play computer games or something else for a maximum of 10 min under the supervision of adult experimenters, to heighten their motivation to participate in the study. Following the completion of the final training session, participants were requested to complete the same baseline assessments as post-training outcome measures. One week after the post-assessment, follow-up data were collected for the same measurements as baseline assessments. Additionally, participants assigned to the PCT were offered CBM-I after post-assessment.

Zeng K., Cao F., Wu Y., Zhang M, & Ding X. (2023). Effects of interpretation bias modification on hostile attribution bias and reactive cyber-aggression in Chinese adolescents: a randomized controlled trial. Current Psychology (New Brunswick, N.j.).

Publication 2023

Adult Motivation Student Supervision

Qualitative Insights on PROM Use in Palliative Care

In addition to the study questionnaire, we conducted semi-structured interviews administered by trained researchers online or by telephone according to an interview guide prepared by our research team. Interviews were conducted by NI, who had interview training and experience in qualitative research. The researcher explained the purpose of the study to the participating institutions. Interviews were conducted between February and April 2020. The interview was administered to gather more insight into specific examples illustrating the usefulness of PROs as well as barriers to the effective collection and use of PROs in palliative care clinical practice; it also helped to gain a better understanding of information on the improvement of processes relevant to the administration of PROMs at each facility.
The interviews included questions relevant to the healthcare providers’ opinions on the use of PROMs for patients receiving palliative care, what the providers felt had been helpful or good about using PROMs for patients receiving palliative care, the usefulness of PROMs in informing care, and improvements in processes for administering PROMs at the healthcare providers’ institution (e.g., staff training and operational methods). The interviews were recorded on an integrated circuit (IC) recorder with the institution’s consent. The coding was compared and two authors (NI, YI) discussed discrepancies to inform the final coding with supervision.

Ito N., Ishii Y., Aoyama M., Abo H., Sakashita A., Matsumura Y, & Miyashita M. (2023). Routine patient assessment and the use of patient-reported outcomes in specialized palliative care in Japan. Journal of Patient-Reported Outcomes, 7, 25.

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Publication 2023

Feelings Health Personnel Palliative Care Patients Proline Supervision

Weakly-Supervised Cervical Dysplasia Grading

As mentioned earlier, automatic cervical dysplasia grading should be done on H &E WSI and focused on the cells of the squamous epithelium, that on LEEP samples and surgical specimens is usually a thin area within the sample tissue. Thus, despite the usual big dimension of digitised slides, which easily become hard and tedious to annotate^{34 (link)}, fully-supervised models for cervical dysplasia may require annotation and labelling of epithelium, besides the usual annotation of relevant areas for diagnosis/classification. However, in general, WSI are not usually publicly available, and, when they are, they have only the associated diagnosis and no detailed pixel-level annotations.
In this sense, and following a common approach in computational pathology, we propose a weakly-supervised methodology for cervical dysplasia grading, based on tiles (small areas within the slide) using different levels of training supervision, in an attempt to leverage a big dataset only partially annotated with full details. In this particular case, we define three different levels of data for training, as represented in Fig. 2:

Labelled slides (LS): each slide labelled only with the slide diagnosis can be abstracted as a set (or bag) of tiles from the epithelial regions (tiles from non-epithelial regions are not used in the development of the DL model); the labelling of each tile is unknown, but it is assumed that the worst of the unknown tile labels corresponds to the bag labelling (slide diagnosis); Epithelial areas are automatically identified using a (deep) segmentation model;

Annotated epithelium (AE): epithelium was delineated and labelled on a subset of the slides; For model development, each labelled epithelial region is considered a set/bag of tiles where the labelling of the set is known but the labelling of the individual tiles is not; The slides with labelled epithelia yield smaller bags than the slides with diagnosis only, which improves the quality of training;

Annotated tiles (AT): within the annotated epithelium, smaller regions of interest were delineated, indicating unequivocal tissue areas of non-neoplastic tissue, LSIL and HSIL, from where tiles with known labels were retrieved.

Figure 2

Annotation levels for training: labelled slides (top), annotated epithelium (middle) and annotated tiles (bottom).

In addition to facilitating labelling, such a scheme can serve two other purposes: acquiring details for training a supervised segmentation model, focusing the assessment of the degree of dysplasia on the regions of interest (ROIs), and adding more information to the tiles to facilitate learning of the classifier.

Oliveira S.P., Montezuma D., Moreira A., Oliveira D., Neto P.C., Monteiro A., Monteiro J., Ribeiro L., Gonçalves S., Pinto I.M, & Cardoso J.S. (2023). A CAD system for automatic dysplasia grading on H&E cervical whole-slide images. Scientific Reports, 13, 3970.

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Publication 2023

Cervical Dysplasia Diagnosis Epithelium High-Grade Squamous Intraepithelial Lesions Low-Grade Squamous Intraepithelial Lesions Neoplasms Operative Surgical Procedures Squamous Epithelial Cells Supervision Tissues

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What is Supervision and why is it important in research?

Supervision refers to the act of overseeing and directing the work of others to ensure tasks are completed effectively and efficiently. It involves providing guidance, feedback, and support to subordinates or team members to help them achieve their goals and maintain high levels of peformance. Effective supervision is crucial in research to optimize workflow, ensure accuracy, and enhance reproducibility.

How can PubCompare.ai help with Supervision in research?

PubCompare.ai allows you to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy. PubCompare.ai helps researchers identify the most effective protocols related to Supervision for their specific research goals, streamlining the research workflow.

What are the different types or variations of Supervision in research?

Supervision in research can take various forms, such as project management, team oversight, quality control, and data validation. Researchers may need to supervise the work of lab technicians, data analysts, or even other researchers to ensure consistent methodologies, accurate data collection, and reliable results. The specific type of Supervision required will depend on the research project and the team structure.

How can Supervision be applied in different research settings?

Supervision is essential across a wide range of research settings, from academic laboratories and clinical trials to pharmaceutical R&D and product development. In academic research, Supervision may involve guiding graduate students and postdocs, while in industry, it may focus on coordinating cross-functional teams and ensuring compliance with regulatory standards. Regardless of the setting, effective Supervision helps to maintain high levels of quality, efficiency, and reproducibility in research.

What are common challenges in implementing Supervision in research and how can PubCompare.ai help?

Some common challenges in implementing Supervision in research include communication breakdowns, resource constraints, and difficulty in identifying the most effective protocols. PubCompare.ai can help by streamlining the protocol screening process, providing AI-driven insights to pinpoint the best protocols, and facilitating collaboration between researchers. By leveraging PubCompare.ai's powerful tools, researchers can overcome these challenges and maintain a high level of Supervision throughout their research projects.

More about "Supervision"

Supervision, the cornerstone of effective management, encompasses the art of overseeing and guiding the work of others to ensure tasks are completed efficiently and with excellence.
This multifaceted process involves providing feedback, support, and direction to subordinates or team members, enabling them to achieve their goals and maintain high levels of peformance.
Supervisors play a crucial role in monitoring progress, identifying and addressing issues, and making strategic decisions to optimize workflow and outcomes.
Effective supervision requires a unique blend of skills, including strong communication, problem-solving, and leadership capabilities.
Supervisors must possess the ability to motivate and develop employees, fostering an environment that nurtures growth and productivity.
This is particularly relevant in the context of scientific research, where protocols and workflows are meticulously designed and executed.
In the realm of experimental biology, the supervision of tasks involving C57BL/6J mice, Sprague-Dawley rats, and other model organisms is paramount.
Researchers must ensure that protocols are followed accurately, data is collected and analyzed rigorously using tools like SPSS software, MATLAB, and Nexus Expression 3 software, and findings are reproducible.
Effective supervision in this context can help optimize research protocols, enhance reproducibility, and ultimately drive scientific discoveries.
Supervision is not limited to the laboratory; it permeates various aspects of our lives, from the corporate world to personal endeavors.
Whether managing a team of Sprague-Dawley rats in a research setting or overseeing a project in a professional environment, the principles of effective supervision remain the same – clear communication, problem-solving, and a commitment to achieving the best possible outcomes.
By embracing the insights and tools provided by supervision, individuals and organizations can unlock their full potential and drive sustained success.