Parameters from preoperative overnight oximetry predict postoperative adverse events.
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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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".