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Anterior Hemiepiphysiodesis of the Distal Tibia: A Step-by-step Surgical Technique Guide

2024· article· en· W4391032290 on OpenAlexaff
Reggie C. Hamdy, Alan W. Katz

Bibliographic record

VenueStrategies in Trauma and Limb Reconstruction · 2024
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeShriners Hospitals for Children - CanadaMontreal Children's Hospital
Fundersnot available
KeywordsMedicineTibiaSurgeryDeformitySurgical planningClubfootDistal tibia

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.271
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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