> Disorders > Mental or Behavioral Dysfunction > Alcohol Use Disorder

Alcohol Use Disorder

Q: What are the different types or variations of AUD?

AUD can vary in severity, ranging from mild to severe. Mild AUD involves drinking that interferes with daily life, while severe AUD is characterized by a physical and psychological dependence on alcohol. There are also subtypes of AUD, such as alcohol abuse and alcoholism, which differ in their specific symptoms and patterns of alcohol use.

Q: How can PubCompare.ai help with AUD research?

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

Q: What are some common challenges with using alcohol and managing AUD?

Some common challenges with using alcohol and managing AUD include difficulty controlling alcohol consumption, experiencing cravings, facing social stigma, and struggling with the physical and psychological effects of addiction. AUD can also lead to financial, relationship, and health problems if left untreated.

Q: How can PubCompare.ai assist in optimizing Alcohol Use Disorder research?

PubCompare.ai can assist in optimizing Alcohol Use Disorder research by helping researchers identify the most effective protocols from the literature. The platform's AI-powered tools can pinpoint critical insights, enable easy comparisons, and help you choose the best approach for your specific research goals. This can enhance reproducibility, accuracy, and the overall quality of your AUD studies.

Alcohol Use Disorder (AUD) is a medical condition characterized by an impaired ability to control alcohol consumption, leading to significant distress or impairment in daily life.
It encompasses a spectrum of problematic drinking behaviors, including alcohol abuse and alcoholism.
AUD can have serious physical, psychological, and social consequences, and is recognized as a chronic, relapsing brain disease.
Understanding and effectively treating AUD is crucial for improving public health and individual wellbeing.
Reserach in this field aims to identify risk factors, develop evidence-based interventions, and enhance recovery outcomes for individuals affected by this disorder.

Most cited protocols related to «Alcohol Use Disorder»

Multidimensional Assessment of Impulsivity and Risky Behaviors

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Publication 2014

Alcoholics Alcohol Use Disorder Diagnosis Eating Disorders Ethnicity Feeding Behaviors Pharmaceutical Preparations Quercus Substance Use

Genome-wide Overlap Across Psychiatric Disorders

Genome-wide significant loci for BD were assessed for overlap with genome-wide significant loci for other psychiatric disorders, using the largest available GWAS results for major depression^{61 (link)}, schizophrenia^{60 (link)}, attention deficit/hyperactivity disorder¹⁰¹, post-traumatic stress disorder¹⁰², lifetime anxiety disorder¹⁰³, Tourette’s Syndrome¹⁰⁴, anorexia nervosa¹⁰⁵, alcohol use disorder or problematic alcohol use^{68 (link)}, autism spectrum disorder¹⁰⁶, mood disorders^{91 (link)} and the cross-disorder GWAS of the Psychiatric Genomics Consortium^{66 (link)}. The boundaries of the genome-wide significant loci were calculated in the original publications. Overlap of loci was calculated using bedtools v2.29.2¹⁰⁷.

Mullins N., Forstner A.J., O’Connell K.S., Coombes B., Coleman J.R., Qiao Z., Als T.D., Bigdeli T.B., Børte S., Bryois J., Charney A.W., Drange O.K., Gandal M.J., Hagenaars S.P., Ikeda M., Kamitaki N., Kim M., Krebs K., Panagiotaropoulou G., Schilder B.M., Sloofman L.G., Steinberg S., Trubetskoy V., Winsvold B.S., Won H.H., Abramova L., Adorjan K., Agerbo E., Al Eissa M., Albani D., Alliey-Rodriguez N., Anjorin A., Antilla V., Antoniou A., Awasthi S., Baek J.H., Bækvad-Hansen M., Bass N., Bauer M., Beins E.C., Bergen S.E., Birner A., Pedersen C.B., Bøen E., Boks M.P., Bosch R., Brum M., Brumpton B.M., Brunkhorst-Kanaan N., Budde M., Bybjerg-Grauholm J., Byerley W., Cairns M., Casas M., Cervantes P., Clarke T.K., Cruceanu C., Cuellar-Barboza A., Cunningham J., Curtis D., Czerski P.M., Dale A.M., Dalkner N., David F.S., Degenhardt F., Djurovic S., Dobbyn A.L., Douzenis A., Elvsåshagen T., Escott-Price V., Ferrier I.N., Fiorentino A., Foroud T.M., Forty L., Frank J., Frei O., Freimer N.B., Frisén L., Gade K., Garnham J., Gelernter J., Pedersen M.G., Gizer I.R., Gordon S.D., Gordon-Smith K., Greenwood T.A., Grove J., Guzman-Parra J., Ha K., Haraldsson M., Hautzinger M., Heilbronner U., Hellgren D., Herms S., Hoffmann P., Holmans P.A., Huckins L., Jamain S., Johnson J.S., Kalman J.L., Kamatani Y., Kennedy J.L., Kittel-Schneider S., Knowles J.A., Kogevinas M., Koromina M., Kranz T.M., Kranzler H.R., Kubo M., Kupka R., Kushner S.A., Lavebratt C., Lawrence J., Leber M., Lee H.J., Lee P.H., Levy S.E., Lewis C., Liao C., Lucae S., Lundberg M., MacIntyre D.J., Magnusson S.H., Maier W., Maihofer A., Malaspina D., Maratou E., Martinsson L., Mattheisen M., McCarroll S.A., McGregor N.W., McGuffin P., McKay J.D., Medeiros H., Medland S.E., Millischer V., Montgomery G.W., Moran J.L., Morris D.W., Mühleisen T.W., O’Brien N., O’Donovan C., Olde Loohuis L.M., Oruc L., Papiol S., Pardiñas A.F., Perry A., Pfennig A., Porichi E., Potash J.B., Quested D., Raj T., Rapaport M.H., DePaulo J.R., Regeer E.J., Rice J.P., Rivas F., Rivera M., Roth J., Roussos P., Ruderfer D.M., Sánchez-Mora C., Schulte E.C., Senner F., Sharp S., Shilling P.D., Sigurdsson E., Sirignano L., Slaney C., Smeland O.B., Smith D.J., Sobell J.L., Hansen C.S., Artigas M.S., Spijker A.T., Stein D.J., Strauss J.S., Świątkowska B., Terao C., Thorgeirsson T.E., Toma C., Tooney P., Tsermpini E.E., Vawter M.P., Vedder H., Walters J.T., Witt S.H., Xi S., Xu W., Yang J.M., Young A.H., Young H., Zandi P.P., Zhou H., Zillich L., Adolfsson R., Agartz I., Alda M., Alfredsson L., Babadjanova G., Backlund L., Baune B.T., Bellivier F., Bengesser S., Berrettini W.H., Blackwood D.H., Boehnke M., Børglum A.D., Breen G., Carr V.J., Catts S., Corvin A., Craddock N., Dannlowski U., Dikeos D., Esko T., Etain B., Ferentinos P., Frye M., Fullerton J.M., Gawlik M., Gershon E.S., Goes F.S., Green M.J., Grigoroiu-Serbanescu M., Hauser J., Henskens F., Hillert J., Hong K.S., Hougaard D.M., Hultman C.M., Hveem K., Iwata N., Jablensky A.V., Jones I., Jones L.A., Kahn R.S., Kelsoe J.R., Kirov G., Landén M., Leboyer M., Lewis C.M., Li Q.S., Lissowska J., Lochner C., Loughland C., Martin N.G., Mathews C.A., Mayoral F., McElroy S.L., McIntosh A.M., McMahon F.J., Melle I., Michie P., Milani L., Mitchell P.B., Morken G., Mors O., Mortensen P.B., Mowry B., Müller-Myhsok B., Myers R.M., Neale B.M., Nievergelt C.M., Nordentoft M., Nöthen M.M., O’Donovan M.C., Oedegaard K.J., Olsson T., Owen M.J., Paciga S.A., Pantelis C., Pato C., Pato M.T., Patrinos G.P., Perlis R.H., Posthuma D., Ramos-Quiroga J.A., Reif A., Reininghaus E.Z., Ribasés M., Rietschel M., Ripke S., Rouleau G.A., Saito T., Schall U., Schalling M., Schofield P.R., Schulze T.G., Scott L.J., Scott R.J., Serretti A., Weickert C.S., Smoller J.W., Stefansson H., Stefansson K., Stordal E., Streit F., Sullivan P.F., Turecki G., Vaaler A.E., Vieta E., Vincent J.B., Waldman I.D., Weickert T.W., Werge T., Wray N.R., Zwart J.A., Biernacka J.M., Nurnberger J.I., Cichon S., Edenberg H.J., Stahl E.A., McQuillin A., Di Florio A., Ophoff R.A, & Andreassen O.A. (2021). Genome-wide association study of over 40,000 bipolar disorder cases provides new insights into the underlying biology. Nature genetics, 53(6), 817-829.

Publication 2021

Alcohol Use Disorder Anorexia Anxiety Disorders Attention Deficit Disorder Ethanol Genome Genome-Wide Association Study Mental Disorders Mood Pervasive Development Disorders

Diagnostic Assessment of DSM-5 Disorders

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Publication 2014

Agoraphobia Alcohol Use Disorder Anxiety Disorders Cannabis Central Nervous System Stimulants Club Drugs Cocaine Conduct Disorder Diagnosis Disorder, Depressive Drug Use Disorders Dysthymic Disorder Hallucinogens Heroin Inhalation Drug Administration Manic Episode Mood Disorders Opioids Panic Disorder Pharmaceutical Preparations Phobia, Social Phobia, Specific Post-Traumatic Stress Disorder Sedatives Solvents Tobacco Products Tobacco Use Disorder Tranquilizing Agents

NESARC-III Test-Retest Reliability Study

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Publication 2014

Adult Agoraphobia Alcohol Use Disorder Antisocial Personality Disorder Anxiety Disorders Asian Americans Drug Use Disorders Dysthymic Disorder Ethanol Ethics Committees, Research Face Hispanics Households Major Depressive Disorder Mania Mental Health Minority Groups Mood Mood Disorders Nicotine Use Disorder Panic Disorder Pharmaceutical Preparations Phobia, Specific Post-Traumatic Stress Disorder Social Anxiety Stress Disorders, Traumatic Substance Abuse Target Population Tobacco Products Tobacco Use Disorder Workers Wounds and Injuries

NAFLD Inclusion Criteria in NIH CRN Studies

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Publication 2009

Adult Alcohol Use Disorder Antibodies, Antinuclear Autoimmune Chronic Hepatitis Child Ethanol Fibrosis Hemochromatosis Hepatitis B Hepatolenticular Degeneration Hypergammaglobulinemia Liver Diseases Metformin Necrosis Non-alcoholic Fatty Liver Disease Nonalcoholic Steatohepatitis Pioglitazone Placebos Primary Biliary Cholangitis SERPINA5 protein, human TimeLine Vitamin E Woman

Most recents protocols related to «Alcohol Use Disorder»

Characteristics of Patients with Comorbid Mental Health and Substance Use Disorders

Table 1 shows the demographic and clinical characteristics of the total sample, and for patients with COD compared to those without. Patients with COD were younger than those without COD, and more patients in the COD group had a lower education level. In addition, those with COD were more likely to have an unstable housing arrangement, and to have higher baseline and follow-up levels of mental distress. We found a significant reduction in mental distress at follow-up in both groups of patients. Moreover, the baseline motivation to change substance use behavior was as high in patients with COD as among patients without COD. With respect to types of SUD diagnoses, the results revealed that patients with COD were less likely than patients without COD to have an alcohol use disorder (F10). Patients with COD were comparatively more likely to have each of the illicit drug use disorders (i.e. cannabis use disorder, sedatives use disorder, opiate use disorder and stimulant use disorder), and polysubstance use. The data also showed that patients with COD had significantly longer treatment stays.

Table 1

Sample characteristics of patients with COD compared to patients without COD.

Variables	Total (N = 611)		COD(n = 289)		Without COD (n = 322)		COD versuswithout COD
Variables	N^a	% or mean (SD)	n	% or mean (SD)	n	% or mean (SD)	p-value	Effect size
Age (years) at intake	610	30.0 (13.9)	288	32.9 (11.6)	322	42.7 (14.1)	< 0.000	0.759
Female	175	28.7	91	31.6	84	26.2	0.139	0.060
Education level low	189	32.6	109	39.5	80	26.3	< 0.001	0.140
Unstable housing arrangement¹	210	35.1	117	41.2	93	29.6	0.003	0.121
Motivation	610	4.25 (0.77)	289	4.26 (0.78)	321	4.24 (0.75)	0.663	0.027
Mental distress baseline	611	2.15 (0.71)	289	2.30 (0.69)	322	2.02 (0.70)	< 0.001	0.403
Mental distress follow-up²	425	1.93 (0.74)	206	2.07 (0.73)	219	1.79 (0.72)	< 0.001	0.386
Improved mental distress³	425	0.22 (0.79)	206	0.22 (0.79)	219	0.23 (0.80)	0.852	0.013
Length stay (days)	611	93.8 (79.6)	289	112.2 (92.2)	322	77.2 (61.9)	< 0.001	0.446
SUD diagnoses⁴	585	95.7
- Alcohol use F10	345	59.0	132	48.4	213	68.3	< 0.001	0.202
- Opioid use F11	111	19.0	64	23.4	47	15.1	0.010	0.107
- Cannabis use F12	216	36.9	128	46.9	88	28.2	< 0.001	0.193
- Sedative use F13	170	29.1	99	36.3	71	22.8	< 0.001	0.148
- Stimulant use F15	188	32.1	105	38.5	83	26.6	0.002	0.127
- Alcohol use only	229	39.1	62	22.7	167	53.5	< 0.001	0.315
- Polysubstance use⁵	134	21.9	77	28.2	57	18.3	0.004	0.118

¹Reference: Owned or rented residence. ² For both patients with and without COD, the mean score of mental distress was lower at follow up (t = 41.15, df = 205, p < 0.001, and t = 36.66, df = 218, p < 0.001, respectively). ³ Calculated by subtracting the mean follow-up score from the mean baseline score. ⁴ More than one diagnosis could be registered for each patient. The most common two-substance combination was cannabis use and stimulant use (n = 113).⁵ Three or more SUD diagnoses

Notes: Percentages in valid percent. Categorical variables presented as valid percentages (%); continuous variables presented as mean (SD). Effect size was measured using Cohen’s d and Cramer’s V, as appropriate

Andersson H.W., Mosti M.P, & Nordfjaern T. (2023). Inpatients in substance use treatment with co-occurring psychiatric disorders: a prospective cohort study of characteristics and relapse predictors. BMC Psychiatry, 23, 152.

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Publication 2023

Alcohol Use Disorder Cannabis Diagnosis Drug Use Disorders Motivation Opiate Alkaloids Opioids Patients Respiratory Diaphragm Sedatives Substance Use Youth

Comorbidity of Substance Use Disorders and Psychiatric Diagnoses

The SUD diagnoses and psychiatric diagnoses according to ICD-10 criteria, were made by a medical specialist or clinical psychologist using standardized clinical interviews and tools.
For the purpose of the current study, information on the dependence-level SUD diagnosis and any co-occurring psychiatric diagnosis was obtained from the medical record. The following binary SUD diagnosis (1 = presence, 0 = absence) were included in analyses: Alcohol use disorder (F10); Opioid use disorder (F11); Cannabis use disorder (F12); Sedatives use disorder (F13); Stimulant use disorder (F15). The psychiatric diagnoses were grouped into the following binary variables (1 = presence, 0 = absence): Mood disorders (F30-F39); Anxiety disorders (F40-F49); Personality disorders (F60-F69); ADHD (F90-F90.0), and other psychiatric diagnoses.

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Publication 2023

Alcohol Use Disorder Anxiety Disorders Cannabis Diagnosis Diagnosis, Psychiatric Disorder, Attention Deficit-Hyperactivity Mood Disorders Opioid Use Disorder Personality Disorders Psychologist Sedatives

Psychiatric Comorbidities in Substance Use Disorder

The sample comprised 611 patients who were included in the prospective cohort study, of whom 289 patients (47.3%) had at least one co-occurring psychiatric diagnosis (F20-F99).
In total, 426 of the patients participated in the follow-up interview 3 months after discharge from treatment (70%), of whom 206 (48.4%) were patients with COD. The follow-up response rate was similar for patients with COD (71.3%) and those without COD (68.3%). Among patients with COD, those who did not respond were more likely younger (OR = 2.54, p = 0.002), with lower education level (OR = 1.77, p = 0.035), and less likely to have an alcohol use disorder (OR = 0.588, p = 0.053). Among patients without COD, those who were lost for follow-up appeared more likely younger (OR = 2.157, p = 0.002), and without a permanent housing situation (OR = 1.694, p = 0.042). About half of those who were reached at follow-up (n = 227) reported they had been in contact with SUD outpatient treatment services during the last month. Slightly fewer patients (n = 194) reported contact with a community health provider. The probability of contact with outpatient SUD services was somewhat higher for patients with COD (58.3%) than for patients without COD (48.6%) (p = 0.047). There was no difference between the groups regarding any contact with community mental health and addiction services.

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Publication 2023

Addictive Behavior Alcohol Use Disorder Care, Ambulatory Diagnosis, Psychiatric Health Services, Outpatient Mental Health Patient Discharge Patients Youth

GWAS of Problematic Alcohol Use

Alternative mRNA splicing associations were inferred from GWAS summary statistics from a study on problematic alcohol use, which we refer to as AUD for simplicity. AUD in this GWAS was defined as a DSM-V AUD diagnosis, a DSM-IV alcohol dependence diagnosis, or a log₁₀ transformed metric of the Alcohol Use Disorders Identification Tests—problem drinking items. This study used 435,563 individuals of European ancestry (Age range = 22–90)^{11 (link)} across three major cohorts: the (1) Million Veteran’s Project, (2) Psychiatric Genomics Consortium and (3) United Kingdom BioBank.

Huggett S.B., Ikeda A.S., Yuan Q., Benca-Bachman C.E, & Palmer R.H. (2023). Genome- and transcriptome-wide splicing associations with alcohol use disorder. Scientific Reports, 13, 3950.

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Publication 2023

Alcoholic Intoxication, Chronic Alcohol Use Disorder Diagnosis Europeans Genome-Wide Association Study RNA, Messenger Veterans

Racial Disparities in Pain and Behavioral Health

Our primary variable of interest was race/ethnicity, which we classified using the US census categories non-Latino White (referred to as White), non-Latino Black (referred to as Black), Hispanic/Latino (referred to as Latino), and non-Latino Asian (referred to as Asian). To address confounding, regression models included the following covariates: age (18–24, 25–34, 35–44, 45-54, 55–64, 65+), sex (males, females), indicator for chronic pain, and indicators for behavioral health disorder diagnoses (anxiety disorder, bipolar disorder, major depression disorder, schizophrenia disorder, alcohol use disorder, cocaine use disorder, cannabis use disorder, and tobacco use disorder). We used ICD-10 codes, as in prior research [50 (link)], to identify chronic pain in the electronic health record.

Flores M.W., Sharp A., Lu F, & Cook B.L. (2023). Examining Racial/Ethnic Differences in Patterns of Opioid Prescribing: Results from an Urban Safety-Net Healthcare System. Journal of Racial and Ethnic Health Disparities.

Publication 2023

Alcohol Use Disorder Anxiety Disorders Asian Persons Behavior Disorders Bipolar Disorder Cannabis Chronic Pain Cocaine Diagnosis Ethnicity Females Hispanics Latinos Major Depressive Disorder Males Schizophrenia Tobacco Use Disorder

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What is Alcohol Use Disorder (AUD)?

What are the different types or variations of AUD?

How can PubCompare.ai help with AUD research?

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

What are some common challenges with using alcohol and managing AUD?

How can PubCompare.ai assist in optimizing Alcohol Use Disorder research?

PubCompare.ai can assist in optimizing Alcohol Use Disorder research by helping researchers identify the most effective protocols from the literature. The platform's AI-powered tools can pinpoint critical insights, enable easy comparisons, and help you choose the best approach for your specific research goals. This can enhance reproducibility, accuracy, and the overall quality of your AUD studies.

More about "Alcohol Use Disorder"

Alcohol Use Disorder (AUD) is a complex medical condition characterized by a diminished ability to control alcohol consumption, leading to significant distress or impairment in daily life.
This disorder encompasses a spectrum of problematic drinking behaviors, including alcohol abuse, alcoholism, and alcohol dependence.
AUD can have severe physical, psychological, and social ramifications, and is recognized as a chronic, relapsing brain disease.
Understanding and effectively treating AUD is crucial for improving public health and individual well-being.
Research in this field aims to identify risk factors, develop evidence-based interventions, and enhance recovery outcomes for individuals affected by this disorder.
Key subtopics include epidemiology, etiology, neurobiological mechanisms, screening and assessment, pharmacotherapy, psychosocial treatments, and relapse prevention strategies.
Synonyms and related terms for AUD include alcohol misuse, problem drinking, alcohol addiction, and alcoholism.
Abbreviations commonly used include AUD, AUD-S (Alcohol Use Disorder Severity), and AUDIT (Alcohol Use Disorders Identification Test).
Relevant assessment tools and technologies used in AUD research and treatment include the EpiTYPER system, OMRON Bronze upper arm blood pressure monitor BP510, Seca 0123 stadiometer, Stata/MP (version 14.1, 64-bit), SPSS (versions 20 and 22.0), FibroScan, and Amplicor.
By incorporating these insights, researchers and clinicians can optimize their Alcohol Use Disorder studies and enhance the understanding and management of this complex disorder.
PubCompare.ai, an AI-powered tool, can further assist in locating the best protocols from literature, pre-prints, and patents, thereby improving reproducibility and accuracy in AUD research.