Lessons From Ximelagatran: Issues for Future Studies Evaluating New Oral Direct Thrombin Inhibitors for Venous Thromboembolism Prophylaxis in Orthopedic Surgery
Bibliographic record
Abstract
Venous thromboembolism is a frequent complication of total hip and knee replacement requiring prophylaxis with anticoagulants. A direct thrombin inhibitor-ximelagatran-did not show advantages over other anticoagulants and it was withdrawn from the market; however, new drugs are being developed. We conducted a systematic review and meta-analysis to identify conditions under which ximelagatran might potentially be superior to current standards. Medline, EMBASE, the Cochrane Library, and grey literature were screened for randomized trials comparing ximelagatran with warfarin or low-molecular-weight heparin for thromboprophylaxis in total hip or knee replacement. Two reviewers independently assessed and extracted data. A meta-analysis with especial attention to statistical heterogeneity was conducted. This study suggested that the risk-benefit profile of ximelagatran-and probably other similar agents-depends on the type of surgery, the initial timing of administration, and probably the dose. These issues should be explicitly explored in future trials evaluating new direct thrombin inhibitors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.101 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".