Benefit-to-harm ratio of thromboprophylaxis for patients undergoing major orthopaedic surgery
Bibliographic record
Abstract
Surgeons consider the benefit-to-harm ratio when making decisions regarding the use of anticoagulant venous thromboembolism (VTE) prophylaxis. We evaluated the benefit-to-harm ratio of the use of newer anticoagulants as thromboprophylaxis in patients undergoing major orthopaedic surgery using the likelihood of being helped or harmed (LHH), and assessed the effects of variation in the definition of major bleeding on the results. A systematic literature search was performed to identify phase II and phase III studies that compared regulatory authority-approved newer anticoagulants to the low-molecular-weight heparin enoxaparin in patients undergoing major orthopaedic surgery. Analysis of outcomes data estimated the clinical benefit (number-needed-to-treat [NNT] to prevent one symptomatic VTE) and clinical harm (number-needed-to-harm [NNH] or the NNT to cause one major bleeding event) of therapies. We estimated each trial's benefit-to-harm ratio from NNT and NNH values, and expressed this as LHH = (1/NNT)/(1/NNH) = NNH/NNT. Based on reporting of efficacy and safety outcomes, most studies favoured enoxaparin over fondaparinux, and rivaroxaban over enoxaparin. However, when using the LHH metric, most trials favoured enoxaparin over both fondaparinux and rivaroxaban when they included surgical-site bleeding that did not require reoperation in the definition of major bleeding. The exclusion of bleeding at surgical site which did not require reoperation shifted the benefit-to-harm ratio in favour of the newer agents. Variations in the definitions of major bleeding may change the benefit-to-harm ratio and subsequently affect its interpretation. Clinical trials should attempt to improve the consistency of major bleeding reporting.
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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 | 0.101 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".