Sleep Complications in Depression, Anxiety, and Psychotic Disorders and Their Treatment
Bibliographic record
Abstract
<P>Sleep disorders are a common problem in the general population as well as the psychiatric population. Although these disorders don’t have the same morbidity and mortality as the Axis I disorders, they are reported frequently and cause much discussion among psychiatrists. Patient find sleep disorders inconvenient at best, crippling at worst; they feel lonely and isolated with a poor night’s sleep. To be up in the middle of the night when their significant other is asleep is profoundly disconnecting and demoralizing. The rest of the family will suffer if their family member is up half the night, or worse, tossing and turning in the same bed. Compounding this clinical issue, many physicians are not trained in sleep medicine and many of the FDA-approved medications have an abuse liability.</P> <h4>ABOUT THE AUTHORS</h4> <P>R. Jeffrey Goldsmith, MD, DFAPA, is a Psychiatrist with the VA Medical Center, Veterans Affairs Medical Center, Dual Diagnosis Services, and Professor of Clinical Psychiatry, Department of Psychiatry, University of Cincinnati. 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. Michael Verenbut, MD, CCFP, FCFP, is Chief Medical Director, Ontario Addiction Treatment Centres, and Assistant Profressor, University of Toronto, Oak Ridges Medical Center. </P> <P>Address correspondence to: R. Jeffrey Goldsmith, MD, DFAPA, 3200 Vine Street, Cincinnati, OH 45220; fax 513-487-6046; or e-mail <a href="mailto:Jeffrey.Goldsmith@va.gov">Jeffrey.Goldsmith@va.gov</a>.</P> <P>The authors disclosed no relevant financial relationships.</P>
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".