Taking Control of Acute Insomnia - Restoring Healthy Sleep Patterns
Bibliographic record
Abstract
is determined solely by the Canadian Sleep Society Available online at www.insomniarounds.ca President Shelly K. Weiss, MD Hospital for Sick Children Toronto, ON Past President and Editor, Insomnia Rounds Helen S. Driver, PhD, RPSGT, DABSM Queen's University, Department of Medicine Sleep Disorders Laboratory, Kingston General Hospital Kingston, ON Vice-President, Research Celyne H. Bastien, PhD Ecole de psychologie/School of Psychology Universite Laval Quebec, QC Vice-President, Clinical Charles Samuels, MD, CCFP, DABSM Centre for Sleep and Human Performance Calgary, AB Secretary/Treasurer Reut Gruber, PhD McGill University, Douglas Institute Montreal, QC Member-at-Large (Technologist) Jeremy Gibbons, BSc, RPSGT Hospital for Sick Children Toronto, ON Member-at-Large (Technologist) Natalie Morin, RPSGT Ottawa, ON Member-at-Large (Student) Christian Burgess Department of Cell and Systems Biology University of Toronto Toronto, ON Member-at-Large (Student) Samar Khoury Hopital du Sacre-Coeur de Montreal Centre d'etudes avancees en medecine du sommeil Montreal, QC Member-at-Large (Membership) Glendon Sullivan, MD Atlantic Health Sciences Centre Saint John, NB Member-at-Large (Physician speciality) Judith A. Leech, MD, FRCPC The Ottawa Hospital Sleep Centre Ottawa, ON Member-at-Large (Dental) Fernanda Almeida, DDS, MSc, PhD University of British Columbia Vancouver, BC Member-at-Large (Newsletter & Website) Stuart Fogel, PhD Centre de Recherche, Institut Universitaire de Geriatrie de Montreal (CRIUGM) Montreal, QC Taking Control of Acute Insomnia – Restoring Healthy Sleep Patterns
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.015 |
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".