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Record W4386019158 · doi:10.1097/med.0000000000000833

Surveys on androgen abuse: a review of recent research

2023· review· en· W4386019158 on OpenAlexaff
Kyle T. Ganson, Jason M. Nagata

Bibliographic record

VenueCurrent Opinion in Endocrinology Diabetes and Obesity · 2023
Typereview
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolysubstance dependenceMedicinePsychological interventionAdverse effectPsychiatryAndrogenPopulationSubstance abuseClinical psychologyEnvironmental healthPharmacologyEndocrinology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To summarize recent survey research on androgen abuse [i.e., anabolic-androgenic steroids (AAS)], including prevalence among international samples, risk factors for use, associated impairments of use, and treatment and interventions for abusers. RECENT FINDINGS: Recent research has documented the prevalence of androgens abuse remains most common among boys and men compared to girls and women, which was stable across nations. However, fewer studies have focused on population-based samples and instead focused on convenience or high-risk samples (i.e., gym goers). Androgen abusers commonly report a history of violent victimization, including adverse childhood experiences. Research continues to document many adverse biological, psychological, and social effects related to androgen abuse, including more than 50% of abusers reporting at least one side effect. Mental health problems and polysubstance use continues to be highly prevalent among androgen abusers. Despite these adverse effects from use, there remains little survey research on treatment and interventions for androgen abusers, representing an important area of future investigation. SUMMARY: Androgen abuse remains relatively common, particularly among boys and men, with adverse health effects regularly occurring. Healthcare professionals and systems can adapt their treatment approaches to focus on reducing harms associated with androgen abuse.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.795
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.350
GPT teacher head0.495
Teacher spread0.145 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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