Operative Management of Lower Extremity Fractures in Patients With Head Injuries
Bibliographic record
Abstract
Treatment of patients with lower extremity fractures and concomitant head injury is controversial. The authors compared reamed intramedullary nailing versus plating of femoral and tibial fractures in patients with polytrauma and concomitant head injury. One thousand five hundred twenty-five patients with head injuries were identified from a prospective trauma database. Of those, 1211 patients sustained severe head injuries (Abbreviated Injury Score >/= 3). One hundred nineteen patients with severe head injuries and lower extremity long bone fractures met the inclusion criteria. Ultimately, four patient groups were identified: Group A, reamed femoral nail (n = 21); Group B, femoral plate (n = 29); Group C, reamed tibial nail (n = 23); and Group D, tibial plate (n = 46). Reamed intramedullary nails did not significantly alter the risk of mortality when compared with plates in femoral (relative risk 0.46; 95% confidence interval, 0.04-4.6) and tibial (relative risk 1.18; 95% confidence interval, 0.05-11.9) fractures. The severity of the initial head injury (Glasgow Coma Scale score) was the strongest predictor of mortality. Functional independence scores between patients with reamed nails and patients with plates were similar at 1 year. Head injury does not seem to be a contraindication to reamed intramedullary nailing in patients with lower extremity fractures. The severity of head injury alone is an important predictor of outcome. A large, randomized trial with sufficient study power is needed to clarify this issue.
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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 | 0.000 | 0.005 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".