Dalteparin Vs Low-Dose Unfractionated Heparin for Prophylaxis Against Clinically Evident Venous Thromboembolism in Acute Traumatic Spinal Cord Injury: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: When venous thromboembolism (VTE) includes deep-vein thrombosis (DVT) and pulmonary embolism (PE), patients with acute traumatic spinal cord injury (SCI) have the highest incidence of VTE among all hospitalized groups, with PE the third most common cause of death. Although low-molecular-weight heparin (LMWH) outperforms low-dose unfractionated heparin (LDUH) in other patient populations, the evidence in SCI remains less robust. OBJECTIVE: To determine whether the efficacy for LMWH shown in previous SCI surveillance studies (eg, routine Doppler ultrasound) would translate into real-world effectiveness in which only clinically evident VTE is investigated (ie, after symptoms or signs present). METHODS: A retrospective cohort study was conducted of 90 patients receiving LMWH dalteparin (5,000 U daily) or LDUH (5,000 U twice daily) for VTE prophylaxis after acute traumatic SCI. The incidence of radiographically confirmed VTE was primarily analyzed, and secondary outcomes included complications of bleeding and heparin-induced thrombocytopenia. RESULTS: There was no statistically significant association (p = 0.7054) between the incidence of VTE (7.78% overall) and the type of prophylaxis received (LDUH 3/47 vs dalteparin 4/43). There was no significant differences in complications, location of VTE, and incidence of fatal PE. Paraplegia (as opposed to tetraplegia) was the only risk factor identified for VTE. CONCLUSIONS: There continues to be an absence of definitive evidence for dalteparin (or other LMWH) over LDUH as the choice for VTE prophylaxis in patients with SCI. Novel approaches to VTE prophylaxis are urgently required for this population, whose risk of fatal PE has not decreased over the last 25 years.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".