Heparin prophylaxis for deep venous thrombosis in a patient with multiple injuries: an evidence-based approach to a clinical problem.
Bibliographic record
Abstract
OBJECTIVE: To demonstrate a clinical decision-making process by which to determine if heparin prophylaxis for deep venous thrombosis (DVT) is appropriate in a specific patient with multiple injuries. DATA SOURCES: A Medline search of the literature. Search terms included trauma, heparin, deep venous thrombosis, thrombophlebitis, phlebitis, and trauma. STUDY SELECTION: Eleven studies were selected from 789 publications using published criteria. Incidence, risk and potential for prophylaxis were established through a structured review process. DATA EXTRACTION: After the structured review, a small number of studies were available for the consideration of incidence (2), natural history (4) and prophylactic therapy (2). DATA SYNTHESIS: The incidence of DVT in a patient with such multiple injuries is significant (58%-63%). The resulting risk of pulmonary embolism was 4.3% with an associated 20% death rate. Prophylaxis with low molecular weight heparin is associated with a statistically and clinically significant risk reduction for DVT when compared with unfractionated heparin and untreated controls. CONCLUSIONS: Few of the multiple available studies concerning trauma, DVT and pulmonary embolism meet reasonable standards to establish clinical validity. Available guidelines for literature evaluation allow surgeons to select relevant articles for consideration. Patients with multiple trauma appear to be at significant risk for DVT. The death rate associated with subsequent pulmonary embolism is significant. There is reasonably good evidence to suggest that low molecular weight heparin will reduce this likelihood without a significant risk of treatment complications.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".