Development of an Algorithm to Identify Preoperative Medical Consultations Using Administrative Data
Bibliographic record
Abstract
BACKGROUND: Preoperative consultation by internal medicine specialists may help improve the care of patients undergoing major surgery. Population-based administrative data are an efficient approach for studying these consultations at a population-level. However, administrative data in many jurisdictions lack specific codes to identify preoperative medical consultations, as opposed to consultations for nonoperative indications. OBJECTIVE: To develop an accurate claims-based algorithm for identifying preoperative medical consultations before major elective noncardiac surgery. RESEARCH DESIGN: We conducted a multicenter cross-sectional study in Ontario, Canada. Preoperative medical consultations identified by medical record abstraction were compared with those identified by linked administrative data (physician service claims, hospital discharge abstracts). SUBJECTS: We randomly selected 606 individuals, aged older than 40 years, who underwent elective intermediate-to-high-risk noncardiac surgery at 8 randomly selected hospitals between April 1, 2002 and March 31, 2004. RESULTS: Medical record abstraction identified preoperative medical consultations in 317 patients (52%). The optimal claims-based algorithm for identifying these consultations was a physician service claim for a consultation by a cardiologist, general internist, endocrinologist, geriatrician, or nephrologist within 4 months before the index surgical procedure. This algorithm had a sensitivity of 90% (95% confidence interval [CI]: 86-93), specificity of 92% (95% CI: 88-95), positive predictive value of 93% (95% CI: 89-95), and negative predictive value of 90% (95% CI: 86-93). CONCLUSIONS: A simple claims-based algorithm can accurately identify preoperative medical consultations before major elective noncardiac surgery. This algorithm may help enhance population-based evaluations of preoperative care, provided that the requisite linked administrative healthcare data are present.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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 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".