Parameters from preoperative overnight oximetry predict postoperative adverse events.
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Continuous home monitoring of oxygen saturation has become a reliable and feasible practice. The objective of this study was to investigate the role of preoperative overnight oximetry in predicting postoperative adverse events. METHODS: Following research ethics board approval, consented patients underwent a preoperative overnight monitoring of oxygen saturation with a portable oximeter. Parameters from the oximetry data were extracted and their predictive performance for postoperative adverse events was evaluated. RESULTS: A total of 573 patients were studied with age: 60±12 years and 45% male. Oxygen desaturation index (ODI), cumulative time percentage with SpO2 <90% (CT90) and mean SpO2 were identified as significant predictors for postoperative adverse events. The privilege sensitivity, optimal predictive and privilege specificity cut-offs were: ODI: >3.0 events/h, >9.2 events/h and > 28.5 events/h; CT90: >0.1%, >1.1% and >7.2%; mean SpO2: <96.2%, <94.6% and <92.7%. The odds ratio for corresponding optimal cut-offs was: ODI 1.9 (95% CI: 1.4,2.7); CT90: 1.7 (95% CI: 1.2,2.4) and mean SpO2: 2.7 (95% CI: 1.9,3.8). The patients classified as high risk by ODI or CT90 or mean SpO2 had a significantly higher rate of postoperative adverse events. For ODI >28.5 vs. ODI ⋝28.5 events/h, the odds ratio adjusted with age, gender, body mass index and American Society of Anesthesiologists physical status was 2.2 (95% CI: 1.3-3.9). CONCLUSION: Patients with mean preoperative overnight SpO2 <92.7% or ODI >28.5 events/h or CT90 >7.2% are at higher risk for postoperative adverse events. Overnight oximetry could be a useful tool to stratify patients for the risk of postoperative adverse events.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it