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Record W4388763432 · doi:10.1302/1358-992x.2023.16.005

PERIPROSTHETIC HIP FRACTURES MANAGED BY REVISION ARTHROPLASTY

2023· article· en· W4388763432 on OpenAlexaboutno aff
Uday Mahajan, Sonu Mehta, Amit Kotecha

Bibliographic record

VenueOrthopaedic Proceedings · 2023
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsPeriprostheticProsthesisMedicineArthroplastyHip arthroplastySurgeryLife expectancyTotal hip arthroplastyPopulationDentistry

Abstract

fetched live from OpenAlex

Abstract Introduction In general the life expectancy of population is improving. This is causing to increase case load of peri-prosthesis fractures after joint replacements. We present our results of peri-prosthesis fracture around hip managed by revision arthroplasty. Methods A retrospective analysis of 24 consecutive patients of periprosthetic hip fracture treated with a revision arthroplasty at Major Trauma Centre between February 2021 and January 2022. Results 12 male and 12 female patients, average age 78 years. 3 fractures around BHR prosthesis, 2 type A, 15 type B and 3 of type C (Vancouver). The surgery was done in an average 6 days after injury (range 1–14). 6 patients died in follow up, 1 patient contracted infection, 2 developed LLD and 1 patient had multiple dislocations. 6 patients had revision using endo-prosthesis. Advanced age with peri-prosthesis fracture has increased risk of mortality (average age 84.5 years). Conclusion Endo-prosthesis replacement had higher risk of dislocation, infection and mortality. Overall patients do well after a revision arthroplasty for periprosthetic hip fracture. Declaration of Interest (b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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