VENOUS THROMBOEMBOLISM RISK MAY DIFFER BETWEEN PELVIC AND ACETABULAR FRACTURES AND CAN BE DEFINED BY SERIAL THROMBELASTOGRAPHY
Bibliographic record
Abstract
Major orthopaedic fractures increase the risk for the development of venous thromboembolism (VTE), which includes pulmonary embolism (PE) and deep vein thrombosis (DVT). These complications continue to be significant causes of preventable morbidity and mortality in trauma patients. Despite thromboprophylaxis to prevent VTE, patients who sustain a pelvic or acetabular fracture continue to have high VTE rates (12% incidence).1 These fractures are often presented together within the literature, despite being anatomically different injuries. Thrombelastography (TEG) is a whole-blood, point-of-care test which provides an overview of the clotting process. Maximal amplitude (MA), from TEG analysis, is a measure of clot strength and values ≥65mm can quantify hypercoagulability and increased VTE risk.2 The aim of this study was to use serial TEG analysis to compare MA values and VTE rates between patients with pelvic fractures and those with acetabular fractures. Potential differences in hypercoagulability profiles between these populations are critical to understand, in order to mitigate VTE risk and potentially inform injury-specific thromboprophylaxis recommendations. Twenty-nine patients (eight females) with a mean age of 49 (SD=19) years were included. Thirteen (45%) sustained acetabular fractures and 16 (55%) sustained pelvic fractures. The average MA values for patients with acetabular fractures were consistently higher than those with pelvic fractures. This was statistically significant on POD3 (p=0.048) and POD5 (p=0.02). There were seven objectively diagnosed VTE events (24%), with five (71%) events being PE and two (29%) DVT. The mean MA value was 70.0mm (±3.9mm) prior to VTE diagnosis. Of the patients with VTE events, four occurred with acetabular fractures and three with pelvic fractures. The VTE rate in acetabular fractures (31%) was higher than pelvic fractures (19%) however this difference was not statistically significant (p=0.45). Four patients (31%) with acetabular fractures had a PE, whereas only one patient (6%) with a pelvic fracture sustained a PE (p=0.08). Though the rate of VTE events in the two populations was not significantly different, the patients who sustained acetabular fractures more commonly suffered a PE and consistently had higher MA values, especially on POD3 and POD5. These results warrant further investigation with a larger sample size, to further inform the potential need for injury-specific VTE risk stratification and thromboprophylaxis recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".