Patterns of Preoperative Consultation and Surgical Specialty in an Integrated Healthcare System
Bibliographic record
Abstract
BACKGROUND: Many patients scheduled for elective surgery are referred for a preoperative medical consultation. Only limited data are available on factors associated with preoperative consultations. The authors hypothesized that surgical specialty contributes to variation in referrals for preoperative consultations. METHODS: This is a cohort study using data from Group Health Cooperative, an integrated healthcare system. The authors included 13,673 patients undergoing a variety of common procedures-primarily low-risk surgeries-representing six surgical specialties, in 2005-2006. The authors identified consultations by family physicians, general internists, pulmonologists, or cardiologists in the 42 days preceding surgery. Multivariable logistic regression was used to estimate the association between surgical specialty and consultation, adjusting for potential confounders including the revised cardiac risk index, age, gender, Deyo comorbidity index, number of prescription medications, and 11 medication classes. RESULTS: The authors found that 3,063 (22%) of all patients had preoperative consultations, with significant variation by surgical specialty. Patients having ophthalmologic, orthopedic, or urologic surgery were more likely to have consultations compared with those having general surgery-adjusted odds ratios (95% CI) of 3.8 (3.3-4.2), 1.5 (1.3-1.7), and 2.3 (1.8-2.8), respectively. Preoperative consultations were more common in patients with lower revised cardiac risk scores. CONCLUSION: There is substantial practice variation among surgical specialties with regard to the use of preoperative consultations in this integrated healthcare system. Given the large number of consultations provided for patients with low cardiac risk and for patients presenting for low-risk surgeries, their indications, the financial burden, and cost-effectiveness of consultations deserve further study.
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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 | 0.000 | 0.000 |
| 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".