Variation in the Practice of Preoperative Medical Consultation for Major Elective Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Patients scheduled for major elective noncardiac surgery frequently undergo preoperative medical consultations. However, the factors that determine whether individuals undergo consultation and the extent of interhospital variation remain unclear. METHODS: The authors used population-based administrative databases to conduct a cohort study of patients, aged 40 yr or older, who underwent major elective noncardiac surgery in Ontario, Canada, between April 2004 and February 2009. Multilevel logistic regression models were used to identify patient- and hospital-level predictors of consultation. RESULTS: Within the cohort of 204,819 patients who underwent surgery at 79 hospitals, 38% (n = 77,965) underwent preoperative medical consultation. Although patient- and surgery-level factors did predict consultation use, they explained only 5.9% of variation in consultation rates. Differences in rates across hospitals were large (range, 10-897 per 1,000 procedures), were not explained by surgical procedure volume or hospital teaching status, and persisted after adjustment for patient- and surgery-level factors. The median odds of undergoing consultation were 3.51 times higher if the same patient had surgery at one randomly selected hospital as opposed to another. CONCLUSIONS: One-third of surgical patients undergo preoperative medical consultation. Although patient- and surgery-level factors are weak predictors of consultation use, the individual hospital is the major determinant of whether patients undergo consultation. Additional research is needed to better understand the basis for this substantial interhospital variation and to determine which patients benefit most from preoperative consultation.
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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 | 0.009 |
| 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 | 0.000 |
| 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 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".