A Matched Cohort Study of Postoperative Outcomes in Obstructive Sleep Apnea
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is associated with increased risk of postoperative complications. The authors investigated whether preoperative diagnosis and prescription of continuous positive airway pressure therapy reduces these risks. METHODS: Matched cohort analysis of polysomnography data and Manitoban health administrative data (1987 to 2008). Postoperative outcomes in adult OSA patients up to 5 yr before (undiagnosed OSA, n = 1,571), and any time after (diagnosed OSA, n = 2,640) polysomnography and prescription of continuous positive airway pressure therapy for a new diagnosis of OSA, were compared with controls at low risk of having sleep apnea (n = 16,277). Controls were matched by exact procedure, indication, and approximate date of surgery. Procedures used to treat sleep apnea were excluded. Follow-up was at least 7 postoperative days. Results were reported as odds ratio (95% CI) for OSA or subgroup versus controls. RESULTS: In multivariate analyses, the risk of respiratory complications (2.08 [1.35 to 3.19], P < 0.001) was similarly increased for both undiagnosed and diagnosed OSA. The risk of cardiovascular complications, primarily cardiac arrest and shock, was significantly different (P = 0.009) between undiagnosed OSA (2.20 [1.16 to 4.17], P = 0.02) and diagnosed OSA patients (0.75 [0.43 to 1.28], P = 0.29). For both outcomes, OSA severity, type of surgery, age, and other comorbidities were also important risk modifiers. CONCLUSIONS: Diagnosis of OSA and prescription of continuous positive airway pressure therapy were associated with a reduction in postoperative cardiovascular complications. Despite limitations in the data, these results could be used to justify and inform large efficacy trials of perioperative continuous positive airway pressure therapy in OSA patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".