The clinical utility of preoperative surgical risk indices and ICU bed allocation on outcomes of noncardiac surgical patients: A cohort study
Why this work is in the frame
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Bibliographic record
Abstract
Summary statement: In non-cardiac surgical patients, respiratory failure index and intensivists’ (expert) opinion predicted postoperative mortality and respiratory failure. Intermediate risk patients allocated to postoperative ICU care vs. surgical high intensity care demonstrated increasing lengths of hospital stay. Background: No guidance exists for allocating post-operative ICU resources for patients undergoing non-cardiac surgery. We determined the predictive value of preoperative risk sores and “expert opinion” in predicting postoperative mortality and complications. Methods: A cohort study involving 403 adults undergoing elective noncardiac surgery and being assessed in a preoperative clinic within a university affiliated tertiary care hospital. Postoperative outcomes included 30-day mortality, respiratory failure at 48-hours, unplanned intubation, cardiac composite score, hospital length of stay, hypotension, hypertension, and delirium. Results: Preoperative respiratory failure index (PRFI) predicted 30-day mortality (OR 1.11, 95% CI 1.04 to 1.19). An intensivist’s opinion predicted respiratory failure 48-hour postoperatively (OR 28.70, 95% CI 7.44 to 110.70). Patients with an equivalent PRFI risk had a longer hospital stay (17.2 v. 8.9 days, P = 0.01), increased respiratory failure risk (P = 0.009), hypertension (P = 0.009), hypotension (P = 0.005) and delirium (P = 0.05) if allocated to an ICU bed versus a high-intensity bed. Conclusions: PRFI predicts 30-day postoperative mortality and cardiac events. A decision to allocate an ICU bed predicted the development of postoperative respiratory failure. Patients with an intermediate PRFI risk and allocated to an ICU demonstrated increasing lengths of hospital stay and morbidity.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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