Bibliographic record
Abstract
Thrombotic events contribute significantly to the morbidity, mortality, and health care costs of patients undergoing major orthopedic surgery. Despite therapy with unfractionated heparin, low-molecular-weight heparin, or warfarin, thrombotic events continue to occur at an unacceptably high rate in populations at risk. The need for improved prophylaxis against venous thromboembolism has resulted in the development of several new antithrombotics, both recently approved and investigational, that target specific steps in the hemostatic pathway. These include direct thrombin inhibitors, agents that inhibit the factor VIIa-tissue factor complex, and selective factor Xa inhibitors. Fondaparinux is the first of the selective factor Xa inhibitors. It was evaluated in the most comprehensive drug development program ever in major orthopedic surgery, culminating in four phase III trials involving more than 7000 enrollees, and is the first agent in its class to be approved by the U.S. Food and Drug Administration and the European Agency for the Evaluation of Medicinal Products. It has been suggested that fondaparinux's superior efficacy may be related to its ability to initiate selective inhibition of factor Xa and its predictable linear pharmacokinetics.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".