Impact of the VTE-PREDICT calculator on clinicians’ decision making in fictional patients with venous thromboembolism: a randomized controlled trial
Bibliographic record
Abstract
Background: After 3 months of anticoagulation for venous thromboembolism (VTE), the decision needs to be made whether to stop anticoagulation or extend treatment indefinitely. The VTE-PREDICT calculator can be used to estimate individual risks of VTE recurrence and bleeding to guide this decision. Objectives: To evaluate the impact of predicted individual risks of recurrence and bleeding on clinicians' decisions on anticoagulation duration and to assess usefulness of the VTE-PREDICT calculator. Methods: A randomized controlled trial and within-subject study was conducted among clinicians treating VTE patients. The clinicians were asked to complete an online survey containing 6 fictional case vignettes. Group A proposed anticoagulant duration for each case without additional information first and subsequently after seeing calculator-predicted risks (within-subject analysis). Group B was directly provided with calculator risks and proposed treatment duration for each case vignette (for comparison with group A results in a randomized controlled trial analysis). Then, group B received questions on usefulness and credibility of the calculator. Results: Forty-five clinicians were assigned to group A and 48 to B. Overall, group A did not propose different anticoagulation durations than group B. However, individual clinicians in group A changed proposed duration in 35% of the cases after seeing the calculator risks. The calculator was considered useful and credible by most clinicians. Conclusion: Overall, use of the VTE-PREDICT calculator did not affect proposed anticoagulation duration. However, individual clinicians frequently changed their proposed duration after using the calculator, especially for patients with high bleeding risk.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".