Sex differences in venous thromboembolism outcomes: findings from the GARFIELD-VTE registry
Bibliographic record
Abstract
BACKGROUND: The association of sex with clinical outcome risk in venous thromboembolism (VTE) is unclear. OBJECTIVE: To investigate sex differences in clinical outcomes and anticoagulation effectiveness in VTE in the GARFIELD-VTE registry. METHODS: Outcomes included all-cause mortality, VTE recurrence, major and any bleeding, myocardial infarction (MI)/acute coronary syndrome (ACS), and stroke/transient ischaemic attack (TIA) over 3 years of follow-up. Hazard ratios were calculated using Cox proportional hazard models with an assessment of sex interactions with parenteral, vitamin K antagonist (VKA), and direct oral anticoagulant (DOAC) therapies. RESULTS: ), and anticoagulant treatment. Females had greater risk of major (adjusted hazard ratio [95% CI (1.25 [1.01-1.55]) and any bleeding (1.32 [1.18-1.47]) than males, but lower risk of recurrent VTE (0.82 [0.72; 0.94]), MI/ACS (0.52 [0.36-0.76]) and stroke/TIA (0.72 [0.52-0.99]). VKA-treated females had greater risk of major (1.69 [1.16-2.48]) and any bleeding (1.43 [1.18-1.73]) than VKA-treated males, while DOAC-treated females had greater risk of any bleeding (1.37 [1.17-1.61]) but not major bleeding (1.22 [0.86-1.72]) than DOAC-treated males. Sensitivity analyses excluding patients with active cancer (N = 9752) yielded similar results. CONCLUSIONS: Compared with males, females with VTE have a greater risk of bleeding, but a lower risk of recurrent VTE, MI/ACS, and stroke/TIA. Sex appears to affect the relationship between VKA and DOAC treatment and bleeding in VTE.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".