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Record W4394903757 · doi:10.1016/j.artd.2024.101369

Hardware Removal and Conversion Hip Arthroplasty via a Single Interval Anterior Approach: Surgical Technique

2024· article· en· W4394903757 on OpenAlexaff
Yaniv Steinfeld, Bheeshma Ravi, Daniel Pincus

Bibliographic record

VenueArthroplasty Today · 2024
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineTotal hip arthroplastyInterval (graph theory)ArthroplastySurgerySurgical proceduresHip arthroplasty

Abstract

fetched live from OpenAlex

The supine 'off-table' anterior-based muscle-sparing (ABMS) approach is an established approach for primary total hip arthroplasty. The approach is performed with the patient positioned supine on a regular operating room table. It combines utilizing the Watson-Jones interval (without disrupting the abductor muscles) with principles of capsular management borrowed from the direct anterior approach. The approach may also be utilized for complex primary and revision hip arthroplasties. One clinical scenario the ABMS approach may be particularly well-suited to is conversion hip arthroplasty when retained hardware requires removal. The approach enables the surgeon to remove proximal femoral hardware and perform hip arthroplasty within the same muscle interval. This is in contrast to direct anterior approach, which entails separate windows being created on either side of the tensor fascia lata muscle to remove hardware and insert hip arthroplasty components, respectively. In this article, we describe our surgical technique for performing conversion total hip arthroplasty with hardware removal (sliding hip screw and plate in the discussed case) via a single interval with the supine off-table ABMS approach.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.249
Teacher spread0.233 · 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.

Study designNot applicable
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

Citations3
Published2024
Admission routes1
Has abstractyes

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