How complete is the evidence for thromboembolism prophylaxis in general medicine patients? A meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
OBJECTIVES: Guidelines recommend pharmacologic prophylaxis for hospitalized medical patients at increased risk of thromboembolism. Despite recommendations, multiple studies demonstrate underutilization. Factors contributing to underutilization include uncertainty that prophylaxis reduces clinically relevant events, as well as questions about the best form of prophylaxis. We sought to determine whether prophylaxis decreases clinically significant events and to answer whether unfractionated heparin (UFH) or low molecular weight heparin (LMWH) is either more effective or safer. DATA SOURCES: The MEDLINE, EMBASE, CINAHL, and Cochrane databases were searched through June 2008. Relevant bibliographies and conference proceedings were reviewed and LMWH manufacturers were contacted. STUDY SELECTION: Randomized trials comparing UFH or LMWH to control, as well as head-to-head comparisons of UFH to LMWH in general medicine patients. DATA EXTRACTION AND ANALYSIS: End points of deep venous thrombosis (DVT), proximal or symptomatic DVT, pulmonary embolism, mortality, bleeding, and thrombocytopenia were extracted from individual trials. Pooled relative risks were calculated using random effects modeling. RESULTS: We identified 8 trials comparing prophylaxis to control, and 6 trials comparing UFH to LMWH. Prophylaxis reduced DVT (relative risk [RR] = 0.55; 95% confidence interval [CI]: 0.36-0.92), proximal DVT (RR = 0.46; 95% CI: 0.31-0.69), and pulmonary embolism (RR = 0.70; 95% CI: 0.53-0.93). Prophylaxis increased the risk of any bleeding (RR = 1.54; 95% CI: 1.15-2.06) but not major bleeding. Across trials comparing LMWH to UFH, there were no differences for any outcome. CONCLUSIONS: Among medical patients, pharmacologic prophylaxis reduced the risk of thromboembolism without increasing risk of major bleeding. The current literature does not demonstrate superior efficacy of UFH or LMWH.
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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 | 0.064 | 0.170 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.033 | 0.051 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".