Thrombophilia testing: does practice follow guidelines?
Bibliographic record
Abstract
In this issue of Blood Advances, using the large, multicenter international Computerized Registry of Patients with Venous Thromboembolism (RIETE) of >100 000 patients with venous thromboembolism (VTE), Cohen et al 1 have investigated the impact of inherited thrombophilia on anticoagulant management and clinical outcomes.Approximately 20% of all patients were tested for inherited thrombophilia, and not surprisingly, clinical characteristics and VTE risk factors differed markedly between tested and untested patients with VTE.Testing for inherited thrombophilia was done more frequently in younger patients, patients with VTE provoked by estrogen use, pregnancy or postpartum, unprovoked VTE, and prior VTE, and less commonly in those with immobilization (≥4 days) and major risk factors such as surgery or active cancer, a pattern that has been recognized over the past decades.2,3
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.019 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".