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Record W2103640661 · doi:10.1080/00952990500328695

Screening for Substance Use Patterns among Patients Referred for a Variety of Sleep Complaints

2006· article· en· W2103640661 on OpenAlexaff
David Teplin, Barak Raz, Jeff Daiter, Michael Varenbut, Meghan Tyrrell

Bibliographic record

VenueThe American Journal of Drug and Alcohol Abuse · 2006
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsToronto Sleep Institute
Fundersnot available
KeywordsInsomniaPsychiatryAddictionSleep (system call)Substance abuseSleep disorderMedicinePsychologyClinical psychology

Abstract

fetched live from OpenAlex

Virtually all psychiatric and substance use disorders are associated with sleep disruption. Studies indicate that psychiatric disorders are related closely to chronic insomnia and that psychoactive substances have acute and chronic effects on sleep architecture. Several aspects of sleep are compromised in individuals taking these substances, ranging from difficulty initiating sleep to difficulty maintaining sleep and hypersomnia. Sleep disturbances are apparent in person taking psychoactive drugs or alcohol and have been found to persist long after withdrawing from these drugs. For some, sleep disturbance can be so severe as to reverse treatment success and precipitate relapse to addiction or dependence. There is increasing evidence that primary insomnia without a concurrent psychiatric disorder is a risk factor for later developing substance use disorders. Patients were asked to complete two brief screening tools, the Michigan Alcohol Screening Test and Drug Abuse Screening Test, to examine substance use patterns among patients referred for a variety of sleep complaints in a sleep disorders clinic. We found that patients who demonstrated a variety of sleep complaints were more likely to have alcohol and drug problems than those in the general populations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.028
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.278
Teacher spread0.254 · 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 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

Citations73
Published2006
Admission routes1
Has abstractyes

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