P.164 Early versus late initiation of chemical venous thromboembolism prophylaxis in adult patients with severe traumatic brain injury: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Patients with severe traumatic brain injury (TBI) are at uniquely high risk of venous thromboembolism (VTE), but the benefits of VTE prophylaxis must be weighed against the risk of intracranial hemorrhage expansion. Current guidelines are heterogenous in their recommendations for chemical VTE prophylaxis (cVTEp) in this high-risk cohort. We conducted a systematic review to identify the optimal timing of cVTEp in severe TBI patients. Methods: We executed a systematic search of the literature to identify adult severe TBI patients treated with cVTEp. Results were pooled, analyzed using random-effects models, and presented as Forest plots and odds ratios. Results: We included 21 studies representing 322,735 patients. The odds of VTE were 0.47 (95% CI: 0.37,0.60) when using the authors’ own criteria for early initiation, and the odds of VTE remained significantly decreased in subgroup analysis (<24h, <48 and <72h). Early VTEp both as defined by authors and in subgroup analysis did not significantly impact the odds of hemorrhage progression or mortality; except for initiation <48h which showed a positive impact on mortality (OR: 0.74, 95% CI: 0.63-0.87). Conclusions: This study supports early initiation of cVTEp in reducing the odds of VTE events without significantly increasing the risk of adverse events.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".