Distal Femur Anterior Hemiepiphysiodesis for Fixed Knee Flexion Deformity in Neuromuscular Patients
Bibliographic record
Abstract
BACKGROUND: In the past decade, distal femur anterior hemiepiphysiodesis for fixed knee flexion deformity has gained popularity. In this study, we aim to systematically review the literature on this technique as a treatment for fixed knee flexion deformity in patients with neuromuscular disorders. METHODS: A systematic review was performed in the following databases: PubMed, Embase, and Medline. The inclusion criteria included anterior hemiepiphysiodesis of the distal femur for patients with neuromuscular disorders and fixed knee flexion deformities. RESULTS: Our search yielded 419 titles. A total of 12 articles were qualified for final review based on the inclusion and exclusion criteria. The total number of patients was 279, with 483 knees corrected. The male to female ratio was 2.1:1, and the mean age of the patients was 11.3 ± 1.4 years (range 4-17 years). The mean preoperative flexion deformity was 23.1° ± 10.0° (range 10°-90°). The mean residual flexion deformity at the final follow-up was 8.6° ± 9.0° (range 0°-32.5°), which corresponds to a statistically significant decrease compared with the preoperative deformity (p < 0.05). CONCLUSION: Anterior hemiepiphysiodesis for fixed knee flexion deformity provides favourable outcomes with low complication rates in patients with neuromuscular disorders. Future studies should focus on comparing the long-term outcomes of the different metal implants used for these operations. LEVEL OF EVIDENCE: Level III. 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".