The clinical utility of preoperative surgical risk indices and ICU bed allocation on outcomes of noncardiac surgical patients: A cohort study
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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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".