Anterior Hemiepiphysiodesis of the Distal Tibia: A Step-by-step Surgical Technique Guide
Bibliographic record
Abstract
Aim: This paper aims to serve as a guide for surgeons to prepare, execute, and perfect anterior hemiepiphysiodesis of the distal tibia (AHDT). Background: Treatment of persistent or recurrent equinus deformity following multiple conservative and surgical interventions in patients with idiopathic clubfoot or neuromuscular conditions can be challenging, and multiple surgical options are presented in the existing literature. Anterior hemiepiphysiodesis of the distal tibia is an option that seems to be safe and efficient in treating this entity. To the best of our knowledge, there is not yet any detailed description of this surgical technique in the English literature. Technique: The AHDT detailed surgical technique includes patient positioning, careful distal anterior tibial approach, placement of guided growth plates, fixation with epiphyseal and metaphyseal screws under fluoroscopic guidance, meticulous closure, and postoperative measures. Conclusion: This guide can be used pre-operatively to plan the surgery, intra-operatively to aid in smooth and safe step progression, and post-operatively to assist in critical critiquing. Clinical significance: By understanding the various stages of the surgery as well as the anatomy, pitfalls can be avoided and AHDT can be performed efficiently. How to cite this article: Katz A, Dumas É, Hamdy R. Anterior Hemiepiphysiodesis of the Distal Tibia: A Step-by-step Surgical Technique Guide. Strategies Trauma Limb Reconstr 2023;18(3):174-180.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".