Femoral Neck Shortening After Internal Fixation of a Femoral Neck Fracture
Bibliographic record
Abstract
This study assesses femoral neck shortening and its effect on gait pattern and muscle strength in patients with femoral neck fractures treated with internal fixation. Seventy-six patients from a multicenter randomized controlled trial participated. Patient characteristics and Short Form 12 and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were collected. Femoral neck shortening, gait parameters, and maximum isometric forces of the hip muscles were measured and differences between the fractured and contralateral leg were calculated. Variables of patients with little or no shortening, moderate shortening, and severe shortening were compared using univariate and multivariate analyses. Median femoral neck shortening was 1.1 cm. Subtle changes in gait pattern, reduced gait velocity, and reduced abductor muscle strength were observed. Age, weight, and Pauwels classification were risk factors for femoral neck shortening. Femoral neck shortening decreased gait velocity and seemed to impair gait symmetry and physical functioning. In conclusion, internal fixation of femoral neck fractures results in permanent physical limitations. The relatively young and healthy patients in our study seem capable of compensating. Attention should be paid to femoral neck shortening and proper correction with a heel lift, as inadequate correction may cause physical complaints and influence outcome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".