Preoperative Laboratory Investigations
Bibliographic record
Abstract
BACKGROUND: Increasing attention has been focused on low-value healthcare services. Through Choosing Wisely campaigns, routine laboratory testing before low-risk surgery has been discouraged in the absence of clinical indications. The authors investigated rates, determinants, and institutional variation in laboratory testing before low-risk procedures. METHODS: Patients who underwent ophthalmologic surgeries or predefined low-risk surgeries in Ontario, Canada, between April 1, 2008, and March 31, 2013, were identified from population-based administrative databases. Preoperative blood work was defined as a complete blood count, prothrombin time, partial thromboplastin, or basic metabolic panel within 60 days before an index procedure. Adjusted associations between patient and institutional factors and preoperative testing were assessed with hierarchical multivariable logistic regression. Institutional variation was characterized using the median odds ratio. RESULTS: The cohort included 906,902 patients who underwent 1,330,466 procedures (57.1% ophthalmologic and 42.9% low-risk surgery) at 119 institutions. Preoperative blood work preceded 400,058 (30.1%) procedures. The unadjusted institutional rate of preoperative blood work varied widely (0.0 to 98.1%). In regression modeling, significant predictors of preoperative testing included atrial fibrillation (adjusted odds ratio [AOR], 2.58; 95% CI, 2.51 to 2.66), preoperative medical consultation (AOR, 1.68; 95% CI, 1.65 to 1.71), previous mitral valve replacement (AOR, 2.33; 95% CI, 2.10 to 2.58), and liver disease (AOR, 1.69; 95% CI, 1.55 to 1.84). The median odds ratio for interinstitutional variation was 2.43. CONCLUSIONS: Results of this study suggest that testing is associated with a range of clinical covariates. However, an association was similarly identified with preoperative consultation, and significant variation between institutions exists across the jurisdiction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.006 |
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; both teacher heads agree on what is shown here.
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".