FP319ASSOCIATION BETWEEN INCIDENT OBSTRUCTIVE SLEEP APNEA AND INCIDENT CHRONIC KIDNEY DISEASE
Bibliographic record
Abstract
Introduction and Aims: Obstructive sleep apnea (OSA) is one of the most common sleep disorders in the general population, however its long-term renal consequences are unclear. We hypothesized that incident OSA (with/without continuous positive airway pressure (CPAP) treatment) would be associated with higher risk of incident chronic kidney disease (CKD) in more than 3 million US Veterans. Methods: In a nationally representative cohort of 3,056,272 OSA negative (OSA-), 21,764 incident, untreated OSA positive (OSA+/CPAP-) and 1,478 incident, CPAP treated OSA positive (OSA+/CPAP+) US Veterans with normal baseline estimated glomerular filtration rate (eGFR), we examined the association of incident OSA with: (1) incidence of decreased kidney function (defined as eGFR <60 ml/min/1.73m2 and 25% decrease in eGFR) and (2) rate of kidney function decline (slopes of eGFR during the follow-up period). Steeper slopes of eGFR were defined as an eGFR decline of more than 5 ml/min/1.73m2/year. Associations were examined in crude and adjusted time-dependent (OSA as time-dependent exposure) Cox models (for time-to-event analyses) and logistic regression models (for slopes), with sequential adjustments for demographic characteristics, baseline eGFR, co-morbidities, blood pressure, body mass, and markers of socioeconomic status, adherence with medical interventions and medication use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".