Thrombosis and hemostasis health in pregnancy: Registries from the International Society on Thrombosis and Haemostasis
Bibliographic record
Abstract
Online patient registries are used to collect data on clinical conditions with rare occurrence or unclear diagnostic and management practices. The success of these registries depends on clear definition of goals, correct identification of patient population/inclusion criteria, availability of appropriate setup and maintenance tools, and the quality of dissemination. The Scientific and Standardization Committee (SSC) for Women's Health Issues in Thrombosis and Hemostasis, one of 20 committees of the International Society on Thrombosis and Haemostasis (ISTH) has developed 6 registries for women's bleeding and thrombotic conditions over the past 2 years and are currently in various stages of progress. Here, we provide information about these registries, including rationale, objectives, and methods for data collection. The aim is to enhance worldwide participation and thus promote the success of these registries. We used ISTH REDCap, a mature and secure Web application for building and managing online surveys and databases, and the ISTH advertising platform to maximize participation. Registries (links and project details available on ISTH and Women's SSC Web sites) include: (1) WiTEAM, project on thrombophilia and placenta-mediated obstetric complications; (2) a registry for disseminated intravascular coagulation in pregnancy; (3) severe congenital protein C deficiency-an obstetric study; (4) obstetric and gynecologic outcomes of women with platelet function disorders; (5) thrombolysis and invasive treatments for massive pregnancy-related pulmonary embolism; (6) pregnancy and exposure to direct oral anticoagulants. The ISTH promotes online registries on women's issues to enhance understanding of current practices, identify knowledge gaps, promote research, and ultimately improve patient safety and quality of life.
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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 | 0.029 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".