Obstructive Sleep Apnea and Incident Diabetes. A Historical Cohort Study
Bibliographic record
Abstract
RATIONALE: Despite emerging evidence that obstructive sleep apnea (OSA) may cause metabolic disturbances independently of other known risk factors, it remains unclear whether OSA is associated with incident diabetes. OBJECTIVES: To evaluate whether risk of incident diabetes was related to the severity and physiologic consequences of OSA. METHODS: A historical cohort study was conducted using clinical and provincial health administrative data. All adults without previous diabetes referred with suspected OSA who underwent a diagnostic sleep study at St. Michael's Hospital (Toronto, Canada) between 1994 and 2010 were followed through health administrative data until May 2011 to examine the occurrence of diabetes. All OSA-related variables collected from the sleep study were examined as predictors in Cox regression models, controlling for sex, age, body mass index, smoking status, comorbidities, and income. MEASUREMENTS AND MAIN RESULTS: Over a median follow-up of 67 months, 1,017 (11.7%) of 8,678 patients developed diabetes, giving a cumulative incidence at 5 years of 9.1% (95% confidence interval, 8.4-9.8%). In fully adjusted models, patients with apnea-hypopnea index (AHI) greater than 30 had a 30% higher hazard of developing diabetes than those with AHI less than 5. Among other OSA-related variables, AHI in rapid eye movement sleep and time spent with oxygen saturation less than 90% were associated with incident diabetes, as were heart rate, neck circumference, and sleep time. CONCLUSIONS: Among people with OSA, and controlling for multiple confounders, initial OSA severity and its physiologic consequences predicted subsequent risk for incident diabetes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".