Anticoagulant therapy for splanchnic vein thrombosis: an individual patient data meta-analysis
Bibliographic record
Abstract
Robust evidence on the optimal management of splanchnic vein thrombosis (SVT) is lacking. We conducted an individual-patient meta-analysis to evaluate the effectiveness and safety of anticoagulation for SVT. Medline, Embase, and clincaltrials.gov were searched up to June 2021 for prospective cohorts or randomized clinical trials including patients with SVT. Data from individual datasets were merged, and any discrepancy with published data was resolved by contacting study authors. Three studies of a total of 1635 patients were included. Eighty-five percent of patients received anticoagulation for a median duration of 316 days (range, 1-730 days). Overall, incidence rates for recurrent venous thromboembolism (VTE), major bleeding, and mortality were 5.3 per 100 patient-years (p-y; 95% confidence interval [CI], 5.1-5.5), 4.4 per 100 p-y (95% CI, 4.2-4.6), and 13.0 per 100 p-y (95% CI, 12.4-13.6), respectively. The incidence rates of all outcomes were lower during anticoagulation and higher after treatment discontinuation or when anticoagulation was not administered. In multivariable analysis, anticoagulant treatment appeared to be associated with a lower risk of recurrent VTE (hazard ratio [HR], 0.42; 95% CI, 0.27-0.64), major bleeding (HR, 0.47; 95% CI, 0.30-0.74), and mortality (HR, 0.23; 95% CI, 0.17-0.31). Results were consistent in patients with cirrhosis, solid cancers, myeloproliferative neoplasms, unprovoked SVT, and SVT associated with transient or persistent nonmalignant risk factors. In patients with SVT, the risk of recurrent VTE and major bleeding is substantial. Anticoagulant treatment is associated with reduced risk of both outcomes.
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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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.061 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".