Validity of administrative data for identification of obstructive sleep apnea
Bibliographic record
Abstract
Summary Obstructive sleep apnea ( OSA ) is a common condition associated with significant morbidity and health‐care utilization. We determined the validity of an algorithm derived from administrative data for identifying OSA using the respiratory disturbance index ( RDI ) as the reference standard. We conducted a retrospective cohort study of adults in Alberta, Canada referred for facility and community‐based sleep diagnostic testing between July 2005 and August 2007. Validity indices were estimated for several case definitions of OSA derived from outpatient physician billing claims and hospital discharge codes. For each algorithm, the sensitivity, specificity, positive predictive value ( PPV ) and negative predictive value ( NPV ) were calculated against several reference standards for OSA ( RDI ≥ 5 h −1 , RDI ≥ 15 h −1 or RDI ≥ 30 h −1 ). For the 2149 patients included in the study, an algorithm requiring one hospital discharge code or two outpatient billing claims identifying OSA in a 2‐year period had a sensitivity of 24.1%, specificity of 67.8%, PPV of 74.8% and NPV of 18.3% (reference standard RDI ≥ 5 h −1 ). When comorbidities were included in the case definition, the specificity was 90.5% and PPV was 83.3% (reference standard RDI ≥ 5 h −1 ). Similar findings were observed using RDI ≥ 15 h −1 and ≥30 h −1 as the reference standard. We identify a claims‐based algorithm that identifies OSA with a high degree of specificity in patients referred for sleep diagnostic testing. This validated algorithm has a good PPV and may be useful when identifying patients with OSA for population studies within a single‐payer health‐care system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".