The Practice of Venous Thromboembolism Prophylaxis in the Major Trauma Patient
Bibliographic record
Abstract
BACKGROUND: The incidence of venous thromboembolism (VTE) without prophylaxis is as high as 80% after major trauma. Initiation of prophylaxis is often delayed because of concerns of injury-associated bleeding. As the effect of delays in the initiation of prophylaxis on VTE rates is unknown, we set out to evaluate the relationship between late initiation of prophylaxis and VTE. METHODS: Data were derived from a multicenter prospective cohort study evaluating clinical outcomes in adults with hemorrhagic shock after injury. Analyses were limited to patients with an Intensive Care Unit length of stay >or=7 days. The rate of VTE was estimated as a function of the time to initiation of pharmacologic prophylaxis. A multivariate stepwise logistic regression model was used to evaluate factors associated with late initiation. RESULTS: There were 315 subjects who met inclusion criteria; 34 patients (11%) experienced a VTE within the first 28 days. Prophylaxis was initiated within 48 hours of injury in 25% of patients, and another one-quarter had no prophylaxis for at least 7 days after injury. Early prophylaxis was associated with a 5% risk of VTE, whereas delay beyond 4 days was associated with three times that risk (risk ratio, 3.0, 95% CI [1.4-6.5]). Factors associated with late (>4 days) initiation of prophylaxis included severe head injury, absence of comorbidities, and massive transfusion, whereas the presence of a severe lower extremity fracture was associated with early prophylaxis. CONCLUSIONS: Clinicians are reticent to begin timely VTE prophylaxis in critically injured patients. Patients are without VTE prophylaxis for half of all days within the first week of admission and this delay in the initiation of prophylaxis is associated with a threefold greater risk of VTE. The relative risks and benefits of early VTE prophylaxis need to be defined to better direct practice in this high-risk population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".