Serious postoperative cardiovascular and respiratory complications in obstructive sleep apnea patients: matched cohort analysis of clinical and administrative data
Bibliographic record
Abstract
Problem: The risk of serious postoperative cardiovascular and respiratory complications (SPCRCs) in patients with obstructive sleep apnea (OSA) is poorly defined. Methods: In this cohort study (n = 21221), patients with clinically diagnosed OSA were matched to controls without OSA to compare the risk of postoperative death and SPCRCs in an administrative database. Results: Compared to non-OSA controls, OSA patients were at increased risk of postoperative respiratory failure both before and after diagnosis with OSA. Prior to diagnosis, OSA patients, particularly those with severe OSA, were also at increased risk of cardiac arrest and SPCRCs . After diagnosis with OSA, except for postoperative respiratory failure, the risk of SPCRC’s was not different from controls. Also, the risk of postoperative death among OSA patients after diagnosis was not different from controls. Other important predictors of SPCRCs and death included admission in an intensive care unit at the time of surgery, a history of congestive heart failure, a higher Charlson comorbidity index score and the type of surgery. Conclusions: OSA was associated with an increased risk of SPCRCs, especially prior to diagnosis and in severe disease. This suggests that screening for and treating OSA in preoperative patients would reduce the risk of SPCRCs. However, the significant influences of the type of surgery and the presence of medical comorbidities on the risks of SPCRCs and death, regardless of the presence of OSA, must be considered in planning efficient and equitable interventions to reduce these risks.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".