Femoral Nerve Palsy After Pelvic Fracture Treated With INFIX
Bibliographic record
Abstract
OBJECTIVE: The treatment of some pelvic injuries has evolved recently to include the use of a subcutaneous anterior pelvic fixator (INFIX). We present 8 cases of femoral nerve palsy in 6 patients after application of an INFIX to highlight this potentially devastating complication to pelvic surgeons using this technique and discuss how it might be avoided in the future. DESIGN: Retrospective chart review. Case series. SETTING: Five level 1 and 2 trauma centers, tertiary referral hospitals. PATIENTS/PARTICIPANTS: Six patients with anterior pelvic ring injury treated with an INFIX who experienced 8 femoral nerve palsies (2 bilateral). INTERVENTION: Removal of internal fixator, treatment for femoral nerve palsy. MAIN OUTCOME MEASUREMENTS: Clinical and electromyographic evaluation of patients. RESULTS: All 6 patients with a total of 8 femoral nerve palsies had their INFIX removed. Variable resolution of the nerve injuries was observed. CONCLUSIONS: Application of an INFIX for the treatment of pelvic ring injury carries a potentially devastating risk to the femoral nerve(s). Despite early implant removal after detection of nerve injury, some patients had residual quadriceps weakness, disturbance of the thigh's skin sensation, and/or gait disturbance attributable to femoral nerve palsy at the time of early final follow-up. LEVEL OF EVIDENCE: Therapeutic level IV. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".