How, what, and why of sleep apnea. Perspectives for primary care physicians.
Bibliographic record
Abstract
OBJECTIVE: To review the need for primary care physicians to screen for patients with obstructive sleep apnea (OSA). QUALITY OF EVIDENCE: Literature was reviewed via MEDLINE from 1993 to 2000, inclusive, using the search term "sleep apnea" combined with "epidemiology," "outcome," and "diagnosis and treatment." Citations in this review favour more recent, well controlled and randomized studies, but findings of pilot studies are included where other research is unavailable. MAIN MESSAGE: Obstructive sleep apnea is a disorder with serious medical, socioeconomic, and psychological morbidity, yet most patients with OSA remain undetected. Primary care physicians have a vital role in screening for these patients because diagnosis can be made only through overnight (polysomnographic) studies at sleep clinics. Physicians should consider symptoms of excessive or loud snoring, complaints of daytime sleepiness or fatigue, complaints of unrefreshing sleep, and an excess of weight or body fat distribution in the neck or upper chest area as possible indications of untreated OSA. CONCLUSION: Current research findings indicate that treating OSA patients substantially lowers morbidity and mortality rates and reduces health care costs. Primary care physicians need more information about screening for patients with OSA to ensure proper diagnosis and treatment of those with the condition.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".