Clinical practice patterns and ascertainment bias for cardiovascular events in a randomized trial: A survey of investigators in the BEST-CLI trial
Bibliographic record
Abstract
Ascertainment bias is a well-recognized source of bias in research, but few studies have systematically analyzed sources of ascertainment bias in randomized trials in which blinding is not possible and endpoint assessment is not protocolized. In the current study, we sought to evaluate differences in the clinical practice patterns of trial investigators with respect to bias in the ascertainment of pre-revascularization patient risk and the incidence of secondary endpoints post-revascularization. We conducted a cross-sectional survey of active investigators ( n = 936) from the Best Endovascular Versus Best Surgical Therapy for Patients with Critical Limb Ischemia (BEST-CLI) trial. The total survey response rate was 19.6% (183/936). Vascular surgeons were more likely than nonsurgical interventionalists to order tests for cardiac complications after both surgical bypass ( p < 0.001) and endovascular revascularization ( p = 0.038). Post-procedure, investigators were more likely to order additional testing for cardiac complications in open surgery versus endovascular cases (7% vs 16% never, 41% vs 65% rarely, 43% vs 17% sometimes, 9% vs 2% always, respectively; p < 0.0001). Significant variation in practice patterns exist in the pre- and post-procedure assessment of cardiac risk and events for patients with CLI undergoing revascularization. Variation in the ascertainment of risk and outcomes according to the type of revascularization procedure and physician specialty should be considered when interpreting the results of clinical studies, such as the BEST-CLI trial. ClinicalTrials.gov Identifier: NCT02060630
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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 | 0.420 | 0.584 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier 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".