MétaCan
Menu
Back to cohort
Record W2138065312 · doi:10.1302/0301-620x.97b8.35641

Reconstruction of chronic abductor deficiency after revision hip arthroplasty using an extensor mechanism allograft

2015· article· en· W2138065312 on OpenAlexaff
Michael Drexler, M. Abolghasemian, Paul R.T. Kuzyk, Tim Dwyer, Yona Kosashvili, David Backstein, Allan E. Gross, Oleg Safir

Bibliographic record

VenueThe Bone & Joint Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsLimpMedicineSurgeryHarris Hip ScoreHip painArthroplasty

Abstract

fetched live from OpenAlex

This study reports the clinical outcome of reconstruction of deficient abductor muscles following revision total hip arthroplasty (THA), using a fresh-frozen allograft of the extensor mechanism of the knee. A retrospective analysis was conducted of 11 consecutive patients with a severe limp because of abductor deficiency which was confirmed on MRI scans. The mean age of the patients (three men and eight women) was 66.7 years (52 to 84), with a mean follow-up of 33 months (24 to 41). Following surgery, two patients had no limp, seven had a mild limp, and two had a persistent severe limp (p = 0.004). The mean power of the abductors improved on the Medical Research Council scale from 2.15 to 3.8 (p < 0.001). Pre-operatively, all patients required a stick or walking frame; post-operatively, four patients were able to walk without an aid. Overall, nine patients had severe or moderate pain pre-operatively; ten patients had no or mild pain post-operatively. At final review, the Harris hip score was good in five patients, fair in two and poor in four. We conclude that using an extensor mechanism allograft is relatively effective in the treatment of chronic abductor deficiency of the hip after THA when techniques such as local tissue transfer are not possible. Longer-term follow-up is necessary before the technique can be broadly applied.

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)0.002
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 score0.790
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.049
GPT teacher head0.279
Teacher spread0.230 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueThe Bone & Joint JournalSame topicOrthopaedic implants and arthroplastyFrench-language works237,207