Sleep in the Substance-using Population
Bibliographic record
Abstract
The effect of any psychoactive substance on an individual’s sleep will depend on whether that substance is a stimulant, depressant, or has other effects on the brain. A schema is portrayed in. The effect of withdrawal from the substance generally will be opposite to the intoxication effects. Psychoactive substances are classified as sedative hypnotics, stimulants, opioids, hallucinogens, and arylcyclohexylamines. ABOUT THE AUTHORS Paul G. Casola, MD, PhD, FRCPC, is a Psychiatrist with the Salvation Army Harbour Light Centre, and Lecturer, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. R. Jeffrey Goldsmith, MD, DFAPA, is a Psychiatrist with the Veterans Affairs Medical Center, Dual Diagnosis Services, and Professor of Clinical Psychiatry, Department of Psychiatry, University of Cincinnati. Jeff Daiter, MD, CCFP, FCFP, is Chief Medical Director, Ontario Addiction Treatment Centres, Oak Ridges Medical Centre, Richmond Hill, Ontario, Canada. Michael Verenbut, MD, CCFP, FCFP, is Chief Medical Director, Ontario Addiction Treatment Centres, and Assistant Profressor, University of Toronto, Oak Ridges Medical Center. Address correspondence to: Paul G. Casola, MD, PhD, FRCPC, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; or email paul.casola@utoronto.ca. The authors disclosed no relevant financial relationships.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".