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 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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".