Accuracy of the Preoperative Assessment in Predicting Pulmonary Risk after Nonthoracic Surgery
Bibliographic record
Abstract
We examined the accuracy of preoperative assessment in predicting postoperative pulmonary risk in a prospective cohort of 272 consecutive patients referred for evaluation before nonthoracic surgery. Outcomes were assessed by an independent investigator who was blinded to the preoperative data. There were 22 (8%) postoperative pulmonary complications. Statistically significant predictors of pulmonary complications (all p < or = 0.005) were as follows: hypercapnea of 45 mm Hg or more (odds ratio, 61.0), a FVC of less than 1.5 L/minute (odds ratio, 11.1), a maximal laryngeal height of 4 cm or less (odds ratio, 6.9), a forced expiratory time of 9 seconds or more (odds ratio, 5.7), smoking of 40 pack-years or more (odds ratio, 5.7), and a body mass index of 30 or more (odds ratio, 4.1). Multiple regression analyses revealed three preoperative clinical factors that are independently associated with pulmonary complications: an age of 65 years or more (odds ratio, 1.8; p = 0.02), smoking of 40 pack-years or more (odds ratio, 1.9; p = 0.02), and maximum laryngeal height of 4 cm or less (odds ratio, 2.0; p = 0.007). Thus, preoperative factors can identify those patients referred to pulmonologists or internists who are at increased risk for pulmonary complications after nonthoracic surgery.
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How this classification was reachedexpand
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".