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Record W4396798819 · doi:10.1302/1358-992x.2024.8.041

OUTCOMES FOLLOWING A TOTAL FEMORAL PLATING TECHNIQUE FOR MANAGEMENT OF PERIPROSTHETIC FRACTURES AROUND STABLE HIP AND KNEE IMPLANTS

2024· article· en· W4396798819 on OpenAlexaboutno aff
Nemandra A. Sandiford, Brad Atkinson, Alex Trompeter, Daniel Kendoff

Bibliographic record

VenueOrthopaedic Proceedings · 2024
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsPeriprostheticMedicineTotal hip replacementPlating (geology)FemurSurgeryOrthodonticsArthroplastyGeology

Abstract

fetched live from OpenAlex

Introduction Management of Vancouver type B1 and C periprosthetic fractures in elderly patients requires fixation and an aim for early mobilisation but many techniques restrict weightbearing due to re-fracture risk. We present the clinical and radiographic outcomes of our technique of total femoral plating (TFP) to allow early weightbearing whilst reducing risk of re-fracture. Methods A single-centre retrospective cohort study was performed including twenty-two patients treated with TFP for fracture around either hip or knee replacements between May 2014 and December 2017. Follow-up data was compared at 6, 12 and 24 months. Primary outcomes were functional scores (Oxford Hip or Knee score (OHS/OKS)), Quality of Life (EQ-5D) and satisfaction at final follow-up (Visual Analogue Score (VAS)). Secondary outcomes were radiographic fracture union and complications. Results Mean OHS and OKS was 50.25, EQ-5D score was >4 for all modalities, VAS was 64.4/100. Radiographs demonstrated bony union in 58% at 3 months and 76% at 6 months. We identified no case of re-fracture however non-union occurred in 4 patients. No other operative complications were identified. Conclusion These results suggest that TFP may be a safe, viable option for management of periprosthetic fractures around stable implants allowing the benefit of early weightbearing, satisfactory outcomes and low re-fracture risk.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.288
Teacher spread0.274 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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