Challenges and lessons from international registries on Women's Health Issues of Thrombosis and Haemostasis
Bibliographic record
Abstract
Registries provide an important tool for data collection, particularly in context of rare conditions, or questions not amenable to investigation by means of a clinical trial. The Subcommittee on Women's Health in Thrombosis and Hemostasis (WHITH); one of 20 Scientific and Standardization Subcommittees (SSCs) of the International Society on Thrombosis and Hemostasis (ISTH) has supported 12 registries related to WHITH since 2016. Despite many successes, some challenges have been observed. In this report, we attempt to review the basic tenets of value in developing a registry, discuss observed challenges, share lessons learned, and finally present our views on a potential formula for success in developing and maintaining a registry in WHITH.
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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 | 0.233 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".