Bibliographic record
Abstract
OBJECTIVE: To describe an approach to sleep apnea for family physicians based on a review of current practice limitations for Canadian family physicians, validated risk prediction tools, and ambulatory sleep apnea technologies. SOURCES OF INFORMATION: Published epidemiologic studies focused on family practice management of sleep apnea, clinical practice guidelines, risk prediction tools for sleep apnea, randomized controlled treatment trials, and the author's community practice audit. Evidence was levels I, II, and III. MAIN MESSAGE: Sleep apnea is commonly encountered in family practice, but many family physicians are unfamiliar with sleep medicine. The pretest probability of sleep apnea can be accurately predicted using any one of several simple risk prediction tools. Screening for other common sleep disorders is important, especially when the pretest probability of sleep apnea is low to intermediate; one-third of sleep apnea patients have additional sleep disorders. The use of home-based rather than laboratory-based diagnostic testing and treatment titration is controversial, but the former setting is often used when referral access is limited. CONCLUSION: There are several tools that allow family physicians to make accurate sleep apnea risk assessments. There is growing evidence to guide home- versus laboratory-based diagnosis and treatment of sleep apnea.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".