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Record W2044812988 · doi:10.1136/ebmh.8.2.54

Lifetime alcohol abstainers and moderate drinkers have a lower lifetime prevalence of mood and anxiety disorders than problem drinkers

2005· letter· en· W2044812988 on OpenAlexaboutno aff
Sami Pirkola

Bibliographic record

VenueEvidence-Based Mental Health · 2005
Typeletter
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyMedicineMoodPsychiatryPopulationAlcohol consumptionAlcoholDemographyEnvironmental healthChemistry

Abstract

fetched live from OpenAlex

Sareen J, McWilliams L, Cox B, et al . Does a U-shaped relationship exist between alcohol use and DSM-III-R mood and anxiety disorders? J Affect Disord 2004;82:113–18.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q Is the lifetime prevalence of mood and anxiety disorders similarly high in people with high lifetime levels of alcohol consumption and in lifetime alcohol abstainers? ### ![Graphic][5] Design: Cross-sectional study. ### ![Graphic][6] Setting: Canada; date of surveys not stated. ### ![Graphic][7] Population: 13 781 respondents from the National Co-morbidity Survey (NCS; n = 6780; 21–54 years old, non-institutionalised individuals) and the Mental Health Supplement of the Ontario Health Survey (OHS; n = 7001; 19–64 years old; province-wide survey), categorised into three groups based on lifetime alcohol consumption: alcohol abstainers, moderate drinkers, and problem drinkers. Abstainers reported never drinking more than 12 drinks in any one year, problem drinkers had received a DSM-III-R diagnosis of alcohol abuse, dependence or a subthreshold diagnosis of “hazardous alcohol use”, and all other respondents were categorised as moderate drinkers. … [1]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2Baffective%2Bdisorders%26rft.stitle%253DJ%2BAffect%2BDisord%26rft.aulast%253DSareen%26rft.auinit1%253DJ.%26rft.volume%253D82%26rft.issue%253D1%26rft.spage%253D113%26rft.epage%253D118%26rft.atitle%253DDoes%2Ba%2BU-shaped%2Brelationship%2Bexist%2Bbetween%2Balcohol%2Buse%2Band%2BDSM-III-R%2Bmood%2Band%2Banxiety%2Bdisorders%253F%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.jad.2002.12.001%26rft_id%253Dinfo%253Apmid%252F15465583%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1016/j.jad.2002.12.001&link_type=DOI [3]: /lookup/external-ref?access_num=15465583&link_type=MED&atom=%2Febmental%2F8%2F2%2F54.atom [4]: /lookup/external-ref?access_num=000224670500013&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.024
GPT teacher head0.298
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2005
Admission routes1
Has abstractyes

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