Development and validation of a Score for Preoperative Prediction of Obstructive Sleep Apnea (SPOSA) and its perioperative outcomes
Bibliographic record
Abstract
BACKGROUND: Postoperative respiratory complications (PRCs) are associated with significant morbidity, mortality, and hospital costs. Obstructive sleep apnea (OSA), often undiagnosed in the surgical population, may be a contributing factor. Thus, we aimed to develop and validate a score for preoperative prediction of OSA (SPOSA) based on data available in electronic medical records preoperatively. METHODS: OSA was defined as the occurrence of an OSA diagnostic code preceded by a polysomnography procedure. A priori defined variables were analyzed by multivariable logistic regression analysis to develop our score. Score validity was assessed by investigating the score's ability to predict non-invasive ventilation. We then assessed the effect of high OSA risk, as defined by SPOSA, on PRCs within seven postoperative days and in-hospital mortality. RESULTS: and comorbidities, including pulmonary hypertension, hypertension, and diabetes. The score yielded an area under the curve of 0.82. Non-invasive ventilation was significantly associated with high OSA risk (OR 1.44, 95% CI 1.22-1.69). Using a dichotomized endpoint, 26,968 (24.8%) patients were identified as high risk for OSA and 7.9% of these patients experienced PRCs. OSA risk was significantly associated with PRCs (OR 1.30, 95% CI 1.19-1.43). CONCLUSION: SPOSA identifies patients at high risk for OSA using electronic medical record-derived data. High risk of OSA is associated with the occurrence of PRCs.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".