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Record W2789499653 · doi:10.1177/112070000101100402

Cortical Strut Allograft in Revision Total Hip Arthroplasty

2001· article· en· W2789499653 on OpenAlexaff
Paul Wong, Anthony E. King, Carol Hutchison, Allan E. Gross

Bibliographic record

VenueHip International · 2001
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicineResorptionSurgeryCortical boneBone resorptionRadiographyAnatomyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Fifty-two patients with significant uncontained but non-circumferential femoral bone loss were reconstructed using cortical strut allografts. The allografts used were deep frozen and irradiated with 2.5 Mrads. The mean age was 65. The average follow-up was 4.8 (1.6–10.0) years. The following radiographic parameters were studied: location and the dimension of the allografts, resorption and incorporation of the allografts, and union between the cortical strut allografts and host bone. Union between the allografts and host bone took an average of 10 months. There were 2 non-unions (union rate 96%) but no graft fractures. Severe graft resorption occurred in two cases. The overall radiographical failure rate was 8% (4/52). The process of incorporation could take over two years to complete. The average length of the strut allografts immediately post-operatively was 154 mm (66–280 mm). The length of the strut allografts at final follow-up was 143 mm (54–258 mm). The percentage decrease in the length of the struts was 8% (0–48%). The mean pre-operative and post-operative Harris hip score was 39.4 and 65.6 respectively. Six of the fifty-two patients had further femoral revision surgery (12%). None of these re-revisions was done for reasons directly related to the cortical struts. Cortical strut allografts are useful to augment uncontained, non-circumferential femoral defects. They can remodel with time to enhance femoral bone stock. They unite consistently to host bone without significant resorption.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.996

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)0.0050.000

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 head0.019
GPT teacher head0.291
Teacher spread0.272 · 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 designObservational
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

Citations5
Published2001
Admission routes1
Has abstractyes

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