Physicians' recommendations for patients who undergo noncardiac surgery.
Bibliographic record
Abstract
OBJECTIVE: To investigate how consulting physicians attempt to modify perioperative cardiac risk for patients who undergo noncardiac surgery by comparing the preoperative cardiac recommendations of consulting physicians in 2 university centres. DESIGN: Retrospective cross-sectional analysis. SETTING: Five hospitals affiliated with 2 Canadian universities. PATIENTS: Three hundred and eight preoperative consultations were evaluated in 297 patients who were 40 years of age or older and scheduled for noncardiac surgery. OUTCOME MEASURES: Cardiac drug recommendations at the preoperative consultation [corrected]; overall recommendations and practice variation between the 2 centres. RESULTS: The greatest changes in drug management suggested by consultants were the initiation of nitrates in 13% of the patients and a decrease in acetylsalicylic acid administration from 27% to 17%. Centre A physicians recommended adding an angiotensin-converting enzyme inhibitor 11% of the time, whereas centre B physicians recommended such an inhibitor in only 1% of the patients (p = 0.001). In patients taking acetylsalicylic acid at the preoperative consultation, Centres A and B physicians recommended withholding the drug 47% and 22% of the time, respectively (p = 0.03). These differences persisted between the 2 centres after controlling for physician estimates of risk. CONCLUSIONS: Consultants frequently recommended perioperative changes in the use of cardiac medications, and there were differences in practice patterns between the 2 centres. These differences may be affecting patient outcomes and highlight the need for randomized clinical trials to determine the impact of perioperative drug administration on bleeding, myocardial infarction and death.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".