Meta regression analysis to indirectly compare dalteparin to enoxaparin for the prevention of venous thromboembolic events following total hip replacement
Bibliographic record
Abstract
BACKGROUND: Patients undergoing elective total hip replacement (THR) surgery are at an increased risk for venous thromboembolic events (VTEs). Dalteparin and enoxaparin are recommended as thromboprophylaxis for at least 10 days in these patients. Even though both agents have proven clinical effectiveness through placebo controlled studies, there have been no head to head trials to assess comparative effectiveness. Indirect statistical techniques were used to compare safety and efficacy between dalteparin and enoxaparin following THR surgery. METHODS: A literature search was conducted from January 1980 to November 2009 for randomized trials evaluating dalteparin or enoxaparin prophylaxis in THR patients. In trials where a common control was used (e.g. placebo), indirect statistical comparisons between dalteparin and enoxaparin were performed using meta regression analysis with active drug as the primary independent variable. RESULTS: A total of nine placebo controlled enoxaparin (n = 5) and dalteparin (n = 4) trials met the inclusion criteria. THR patients treated with enoxaparin or dalteparin had a 50% VTE risk reduction compared to the placebo control (RR = 0.50, p < 0.001). This benefit was achieved without a significant increase in the risk for major bleeds (RR = 1.19, p = 0.76), heparin induced thrombocytopenia (HIT) (RR = 1.13, p = 0.83) or death (RR = 0.72, p = 0.59). The indirect comparison was not able to find significant differences between enoxaparin and dalteparin in terms of VTEs (p = 0.36), major bleeds (p = 0.45), HIT (p = 0.48) and death (p = 0.86). CONCLUSIONS: The findings suggested comparable safety and efficacy between dalteparin and enoxaparin in TKR patients. Therefore, treatment decisions should be based on other considerations, such as patient or physician preference, ease of administration and cost.
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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 | 0.029 | 0.065 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.053 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".