Gender imbalance and risk factor interactions in heparin-induced thrombocytopenia
Bibliographic record
Abstract
Heparin-induced thrombocytopenia (HIT) is caused by antibodies against a "self" protein-platelet factor 4-bound to heparin. We observed an overrepresentation of the female gender in 290 patients who developed HIT after cardiac or orthopedic surgery compared with the representation found in national databases (study 1). Therefore, we investigated gender imbalance in HIT by logistic regression analysis of a randomized controlled trial of unfractionated heparin (UFH) and low-molecular-weight heparin (LMWH) (study 2), and we analyzed individual patient data from 7 prospective studies comparing HIT frequency between UFH and LMWH, evaluating effects of gender, heparin (UFH vs LMWH), and patient type (surgical vs medical) (study 3). All 3 studies showed female overrepresentation, which for study 3 was a common odds ratio (OR) of 2.37 (95% confidence interval [95% CI], 1.37-4.09; P = .0015). Study 3 also showed an interaction between gender, heparin, and patient type. Although UFH was more likely than LMWH to cause HIT (P < .0001), this effect was predominantly seen in women compared with men (common OR, 9.22 vs 1.83; P = .020) and in surgical patients compared with medical patients (common OR, 13.93 vs 1.75; P = .005). We conclude that females are at greater risk for HIT and that using LMWH to prevent HIT may have greatest absolute benefit in females undergoing surgical thromboprophylaxis.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".