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Equipoise

Equipoise is a synthetic anabolic-androgenic steroid commonly used in research and clinical settings.
It is known for its potential to enhance muscle growth, increase strength, and improve overall athletic performance.
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This enhances research accuracy and reproducibility, enabling more reliable findings on the effects and applications of Equipoise.

Most cited protocols related to «Equipoise»

The development of the consensus statement was underpinned by a five-year research project funded by the Canadian Institutes of Health Research [14] (link). The project used a mixed methods approach incorporating both empirical work and ethical analysis. The empirical work included interviews with key informants, review of published CRTs [15] (link), a survey of trialists, and a survey of REC chairs. Based on the empirical work, as well as the practical experiences of research team members, the team identified six questions specific to CRTs in need of further analysis: How should research participants be identified? From whom, how, and when must informed consent be obtained? Does clinical equipoise apply? How does one determine if the benefits outweigh the risks? Who are gatekeepers, and what are their responsibilities? How ought vulnerable groups be protected [16] (link)? The research team conducted an ethical analysis of each issue, which led to a series of discussion papers laying out principles, policy options, and rationales for proposed ethics guidelines [17] (link)–[20] (link). The research team posted these papers on a wiki (http://crtethics.wikispaces.com) and publicized the wiki in the discussion papers and surveys.
To develop the consensus statement from this process, the research team organized a two-and-a-half-day meeting of a multidisciplinary expert panel that took place in Ottawa, Canada, in November 2011. The research team identified the constituencies and perspectives that needed to be represented within the expert panel, including ethicists, cluster trialists, consumer representatives, RECs, policy makers, funding agencies, and journal editors. Potential expert panel members were identified by consultation with colleagues, via searches of the relevant literature and the Internet, and from respondents in the key informant interviews, trialist survey, and REC chair survey. In addition to six of the members of the research team, 26 external individuals were approached, of whom 13 agreed to participate. (See Text S2 for a list of the 19-member expert panel.) External members were invited as individuals rather than as representatives of their home organizations.
The research team made the discussion papers available to the expert panel in advance of the meeting. The first day of the consensus process was an open meeting with a simultaneous webcast, attended by individuals from the same constituencies and sources used to identify the expert panel. Eighty people participated in person, and a further 20 participated by webcast. The research team presented the results of the empirical studies and the ethical analyses of the six questions, and three expert discussants and the audience commented on the presentations. The open meeting served to further familiarize the expert panel with the content of the materials developed by the research team, and allowed them to hear issues raised about the materials by the broader audience. Video of the open portion of the consensus meeting is available via YouTube (http://www.youtube.com/user/mtaljaard55).
Over the next one and a half days the members of the expert panel met in closed session to discuss the identified issues and to develop recommendations. The expert panel was chaired by Professor Martin Eccles, an experienced small group leader with expertise in chairing guideline development groups. Initial discussions established the “rules of engagement” for the expert panel process. The expert panel agreed about how debate should be conducted and how they wanted the chair to run the process. The expert panel agreed to achieve consensus, where possible, through discussion and would document disagreements; they did not wish to use a majority voting system. Draft recommendations based upon the background papers were presented to the expert panel, and members were asked to identify issues in need of clarification and discussion. Full discussion of these issues was facilitated by the chair with the aim of achieving consensus on the underlying principles, but not necessarily specific wording. All expert panel members actively participated in the discussion. Some draft recommendations were substantially revised during the process. There were no substantive disagreements requiring presentation of dissenting views.
A writing group, consisting of seven members of the research team, then reviewed the results of the meeting and produced a first draft of the consensus statement. The writing group circulated the draft to the expert panel in December 2011 and asked for comments on both the principles and specific wording of the recommendations. Responses were received from all participants, and a point-by-point response to all comments (available on request) was produced and the draft consensus statement revised accordingly. In February 2012, the writing group posted the revised consensus statement on the wiki and invited the expert panel, participants of the open meeting, respondents in the key informant interviews, trialist survey, and REC chair survey, and other contacts of the research team to comment. Again, the writing group produced a point-by-point response to all comments (available on request) and revised the consensus statement. In June 2012, the final draft of the consensus statement was sent to the expert panel for approval, which was given by all members with no dissention.
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Publication 2012
Chemoradiotherapy Equipoise Ethicists Hearing Policy Makers
The nutrient goals for the four diet groups were: 20% fat, 15% protein, and 65% carbohydrates (low-fat, average-protein); 20% fat, 25% protein, and 55% carbohydrates (low-fat, high-protein); 40% fat, 15% protein, and 45% carbohydrates (high-fat, average-protein); and 40% fat, 25% protein, and 35% carbohydrates (high-fat, high-protein). Thus, two diets were low-fat and two were high-fat, and two were average-protein and two were high-protein, constituting a two-by-two factorial design. The four diets also allowed for a dose–response test of carbohydrate intake that ranged from 35 to 65% of energy. Other goals for all groups were that the diets should include 8% or less of saturated fat, at least 20 g of dietary fiber per day, and 150 mg or less of cholesterol per 1000 kcal. Carbohydrate-rich foods with a low glycemic index were recommended in each diet. Each participant’s caloric prescription represented a deficit of 750 kcal per day from baseline, as calculated from the person’s resting energy expenditure and activity level.
Blinding was maintained by the use of similar foods for each diet. Staff and participants were taught that each diet adhered to principles of a healthful diet29 (link) and that each had been recommended for long-term weight loss, thereby establishing equipoise.1 (link),2 (link),26 (link) Investigators and staff who measured outcomes were unaware of the diet assignment of the participants.
Group sessions were held once a week, 3 of every 4 weeks during the first 6 months and 2 of every 4 weeks from 6 months to 2 years; individual sessions were held every 8 weeks for the entire 2 years. Daily meal plans in 2-week blocks were provided (see the Supplementary Appendix). Participants were instructed to record their food and beverage intake in a daily food diary and in a Web-based self-monitoring tool that provided information on how closely their daily food intake met the goals for macronutrients and energy. Behavioral counseling was integrated into the group and individual sessions to promote adherence to the assigned diets. Contact among the groups was avoided.
The goal for physical activity was 90 minutes of moderate exercise per week. Participation in exercise was monitored by questionnaire30 (link) and by the online self-monitoring tool.
Publication 2009
ARID1A protein, human Beverages Carbohydrates Cholesterol Diet Dietary Fiber Eating Energy Metabolism Equipoise Fat-Restricted Diet FAT1 protein, human Food Infantile Neuroaxonal Dystrophy Macronutrient Nutrients Proteins Saturated Fatty Acid Teaching Therapy, Diet
We implemented propensity score-based methods in both the empirical example and the simulation studies to account for measured confounders. The propensity score was estimated as the predicted probability of statin exposure during the first trimester using logistic regression models. In the empirical study, all the confounders described above (under Empirical example) were included in the propensity-score model, while in the simulation studies the ten confounders, c1-c10, were included in the propensity-score models. After propensity-score estimation, the following three approaches were used to derive adjusted associations between statin exposure and congenital malformations.
We excluded the observations from the non-overlapping regions of the propensity-score distributions among exposed and unexposed populations before conducting propensity-score full-matching, stratification and SMR weighting. This step, also referred to as ‘trimming’, ensures exclusion of patients who will always or never receive therapy because of indications or contraindications and focuses the estimation of treatment effects in a population with clinical equipoise.22 (link) The ability of propensity score-based approaches to allow researchers to measure treatment effects in a population with clinical equipoise through trimming is one of its great strengths over traditional multivariable outcome regression models.
Publication 2017
Congenital Abnormality Equipoise Hydroxymethylglutaryl-CoA Reductase Inhibitors Patients
The steering committee (M.K. (chair), D.d.L., J.B., P.B. and P.R.) defined disease conditions (see ESM) and identified ten European panel members who were internationally established paediatric MV investigators with recent peer-reviewed publications (last 10 years). An electronic literature search in PubMed and EMBASE (inception to September 1, 2015) was performed using a combination of medical subject heading terms, text words related to MV and disease-specific terms. All panel members screened the references for eligibility, defined by (1) age <18 years, (2) describing non-invasive or invasive respiratory support, and (3) type of design (i.e. any type of clinical study except for case-series and reports). Publications were excluded if they described diseases exclusively linked to the perinatal period. The proposal by Chatburn (ESM, Table 2) was used for ventilator taxonomy [3 (link), 4 (link)].
Recommendations were drafted by all panel members, and subsequently discussed at a two-day meeting in Rome, Italy (September 2015). This resulted in a final set of recommendations, subjected to electronic voting (December 2015) using the Research and Development/University of California, Los Angeles (RAND/UCLA) appropriateness method scale [5 ]. Recommendations were scored from 1 (complete disagreement) to 9 (complete agreement). Median score (95% confidence interval) was calculated after eliminating one lowest and highest value. Recommendations were labelled “strong agreement” (median 7–9 and no score <7), “equipoise” (median 4–6) or “disagreement” (median 1–3). Recommendations without “strong agreement” were rephrased. Revised recommendations retaining “strong agreement” after the second electronic voting (February 2016) were labelled “weak agreement” and the percentage of agreement (number of individual scores ≥7 divided by 15) quantified the level of disagreement. As it was expected a priori that there would be very few RCTs or systematic reviews, it was decided by the steering committee to keep the consensus guideline descriptive and not use the GRADE system [6 (link)].
Publication 2017
Debility Eligibility Determination Equipoise Europeans Respiratory Rate

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Publication 2019
Diagnosis Equipoise Patients Pharmaceutical Preparations

Most recents protocols related to «Equipoise»

The patients were subjected to CCUS plus ABG based algorithm in which we collected information about LUS patterns , TTE (Trans-thoracic Echocardiography), IVC along with ABG on admission to ICU. On the basis of the findings as described in Figure 1 the patients were classified into one of the described pathophysiological domains. The bedside CCUS plus ABG based diagnosis was made by one of the three intensivists, who was present at the time of admission (all with equipoise training in CCUS) . The diagnosis was noted on data collection sheet and then put in a sealed envelope. Once the Chest X-ray was done, the image was sent to two independent physicians not directly related to patient care , who made the diagnosis on the basis of Chest X ray based algorithm as described in Figure 2 and diagnosis was noted on data collection sheet and then put in a sealed envelope. The team analysing the two algorithms were different. The composite diagnosis is the final diagnosis made by two critical care consultants at the end of 48 hours after carefully interpreting clinical and investigational data which included CT scan, Echocardiography and blood investigations. Once the study was completed , the sealed envelopes were opened and correlation of the CCUS plus ABG based algorithm vs Composite diagnosis, CxR algorithm vs Composite diagnosis and CCUS vs CxR was done for each of the pathophysiologic condition. We collected information about patient demographics (age, gender), primary admission source, severity of illness (SOFA), need for intubation, form of mechanical ventilation, vasopressor need , ICU outcome and length of ICU stay. We calculated the diagnostic test properties of each of these algorithms with composite diagnosis, percent agreement and percent agreement beyond chance for each of these algorithms and final correlation of these algorithms for each of the five defined pathophysiological diagnosis (Figure 1).
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Publication 2023
BLOOD Critical Care Diagnosis Echocardiography Equipoise Gender Intubation Mechanical Ventilation Patients Physicians Radiography, Thoracic Tests, Diagnostic Vasoconstrictor Agents X-Ray Computed Tomography X-Rays, Diagnostic

Systematic Review: PubMed was searched for papers on vitamin D and dementia. The literature revealed that although vitamin D deficiency has been associated with higher risk of dementia, the role of supplementation remains in equipoise. Thus, we explored longitudinal associations between vitamin D supplementation and incident dementia in a sample of 12,388 dementia‐free older adults.

Interpretation: Exposure to vitamin D supplementation was associated with a 40% lower dementia incidence rate than no exposure, providing strong support for supplementation. The results were consistent across three vitamin D formulations. The effect of vitamin D exposure on the rate of incident dementia differed significantly across the strata of sex, cognitive status, and apolipoprotein E (APOE) ε4 status.

Future Directions: Future trials should include a more ethnoracially diverse sample, assess baseline vitamin D levels, and account for sun exposure, in addition to sex, baseline cognitive status, and APOE genotype.

Publication 2023
Aged Apolipoprotein E4 Apolipoproteins E Cognition Equipoise Ergocalciferol Genotype Presenile Dementia Vitamin D Deficiency
Interviews were conducted by MJ (a female final year BSc honours undergraduate student with prior research internship experience and who first undertook a training module in qualitative research) and JP (a female paediatric occupational therapist with many years of research experience including experience of qualitative interviewing, and an MPhil). Interviewees did not have a prior working relationship with those interviewed but introductions were made at the start of the interview process. Participants were made aware of the goals of the research at the start of the interview. In terms of reflexivity, JP had led on the course development but was keenly aware of the need to avoid bias towards a favourable outcome in her questioning and interpretation. MJ had not been involved in development of the course and had no prior detailed knowledge of the research field; she was therefore in a position of equipoise.
Interviews took place either face to face in a quiet office setting or by telephone, at the convenience of the interviewee. They were held on a 1:1 basis, with no observers or other non-participants present. No repeat interviews were undertaken.
Interview content was shaped by a topic guide; this was not formally pilot tested but was reviewed by research team members prior to implementation. Interviews began with discussion about the participants’ professional background and prior knowledge of the subject area. This was followed by consideration of what the participant had gained from the training and how it had influenced, or could influence, their practice. The course structure and content were also discussed. Interviews were audio recorded, transcribed verbatim and pseudonymised prior to analysis. Due to the straightforward nature of the interviews, separate field notes were not undertaken and transcripts/findings were not returned to participants for comment.
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Publication 2023
ARID1A protein, human Equipoise Face Medical Internship Occupational Therapist Reflex Student Woman
Regarding baseline characteristics of the patients, continuous variables were reported as mean with standard deviation (SD) while categorical variables were reported as number with percentage. Multiple imputation by chained equation was used to impute missing baseline covariates [14 , 15 (link)]. Each missing datum was imputed using known or imputed variables by five times. Five complete datasets were generated and analyzed independently, and the analyzed results were pooled to an overall estimate by using the Rubin’s rule [16 ]. One-to-one propensity score matching was performed to compare patients who had initiated SGLT2i or GLP-1RA at clinical equipoise. A logistic regression model was fitted to estimate the propensity score of patients by using the baseline covariates. Patients in SGLT2i and GLP-1RA groups were pairwise matched using the propensity score with a caliper of 0.001. An absolute standardized mean difference (ASMD) ≤ 0.1 of baseline covariate indicated the covariate balance between the two groups [17 (link)].
Each included patient had complete follow-up and was observed from the index date until any of the following: (i) the occurrence of events; (ii) death; (iii) addition or switching of treatments to another study exposures (e.g., patients who were initiated on SGLT2i first and switched to GLP-1RA were censored at the start date of GLP-1RA, and vice versa); or (iv) the date of the end of study (i.e., 31st December 2020), whichever was earlier. Cox proportion hazard regression models were constructed to estimate hazard ratio (HR) and 95% confidence interval (CI) of each outcome between the two groups. In addition, incidence rate ratio (IRR) for each outcome was estimated by Poisson regression models.
Subgroup analyses were performed by stratification according to sex (male and female), age group (< 60 and ≥ 60 years), HbA1c (< 8 and ≥ 8%), history of stroke, history of AF, and duration of diabetes (< 10 and ≥ 10 years). Tests for interaction between the novel anti-diabetic medications and subgroups were conducted.
Sensitivity analyses were conducted to check whether consistent results were obtained with different analytic approaches: (i) analysis without censoring on switching treatments; (ii) inclusion of patients with follow-up duration ≥ 1 year; (iii) inclusion of patients with at least two dispensing records within 12 months; (iv) inclusion of only patients who were initiated on SGLT2i or GLP-1RA in or after 2015; (v) ‘as-treated’ analysis, censoring when patients stopped being dispensed the index treatment; (vi) analysis using regression adjustment to control for confounders instead of propensity score matching; and (vii) analysis without adjustment of covariates using either propensity score matching or regression adjustment.
All statistical analyses were performed using Stata version 14.0 (StataCorp, College Station, Texas). p values < 0.05 were considered statistically significant. The current study was reported according to the guideline of STrengthening the Reporting of OBservational studies in Epidemiology (STROBE).
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Publication 2023
Age Groups Cerebrovascular Accident Diabetes Mellitus Equipoise Hypersensitivity Males Patients Pharmaceutical Preparations Woman
Patients became eligible when they were first documented to be receiving oxygen with inspired oxygen fraction (FiO2) of 0.4 or more via non-rebreather mask, noninvasive positive pressure ventilation (NIV), or high-flow nasal cannula (HFNC), within 24 h of ICU admission. We excluded patients with prior invasive ventilation during the same ICU admission, goals of care precluding invasive ventilation, ICU admission from the operating room, or a tracheostomy. Patients were also excluded when equipoise was less certain at the moment of eligibility, defined as a Glasgow Coma Scale (GCS) motor component of less than 4, or a partial pressure of carbon dioxide (pCO2) of 60 or more with pH of 7.20 or less [33 (link)]. Patients were not excluded if these characteristics developed during the follow-up period, after initial inclusion. Wherever oxygen flow was available but FiO2 was not (for example, non-rebreather masks), FiO2 was estimated using the validated equation: FiO2 = 0.21 + (oxygen flow in liters per minute)*0.03 [34 (link)]. Further details are available in Additional file 1: (§4, Table e2).
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Publication 2023
Carbon dioxide Eligibility Determination Equipoise Goals of Care Intermittent Positive-Pressure Ventilation Nasal Cannula Oxygen Partial Pressure Patients Tracheostomy

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More about "Equipoise"

Equipoise, also known as Boldenone Undecylenate, is a popular synthetic anabolic-androgenic steroid (AAS) commonly used in research and clinical settings.
This steroid is prized for its potential to enhance muscle growth, increase strength, and improve overall athletic performance.
Researchers can optimize their Equipoise studies by leveraging the powerful comparison tools and AI-driven insights provided by PubCompare.ai's platform.
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Researchers should also be aware of the legal status and regulations surrounding the use of Equipoise, which may vary by region or jurisdiction.
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