Timing of pharmacological thromboprophylaxis following traumatic brain injury: a UK-wide survey of clinical practice
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) is associated with an increased risk of venous thromboembolism (VTE), which can complicate recovery from TBI, lead to long-term reductions in quality of life, and occasionally be fatal. There is no high-quality evidence to support recommendations for optimal timing, dosing or type of pharmacological thromboprophylaxis (PTP). This study aimed to characterise current clinical practice among healthcare professionals in the UK and Ireland regarding VTE prophylaxis in patients with TBI, to inform research. METHODS: An online survey was distributed to healthcare professionals across the UK and Ireland. The survey addressed types and timing of PTP in TBI patients, as well as factors influencing decision-making, use of imaging, ward-based scenarios, and local protocols. RESULTS: Responses were obtained from 61 individuals from 26 tertiary centres with neuroscience units. There was a heterogenous response with regards to the timing of PTP following TBI across all clinical scenarios, however, the most common factors contributing to decision making before starting VTE prophylaxis included progression of intracranial haemorrhage, new intracranial haemorrhage and prevention of VTE events. 85% of respondents agreed there is no high-quality evidence on the timing of starting pharmacological thromboprophylaxis after an acute TBI. DISCUSSION: This study underscores the lack of consensus for VTE prophylaxis in TBI patients. There is a pressing need for a randomised control trial to guide the optimal timing of PTP following TBI to improve patient care.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".