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Record W4402802094 · doi:10.1177/11207000241282111

The push-through total femoral prosthesis for revision of a total hip or knee replacement with extreme bone loss

2024· article· en· W4402802094 on OpenAlexaboutno aff
Sancar Bakırcıoğlu, Muhammed Abdulkadir Bulut, Melih Oral, Ömür Çağlar, Bülent Atılla, A. Mazhar Tokgözoğlu

Bibliographic record

VenueHip International · 2024
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProthesisProsthesisSurgeryHarris Hip ScoreRange of motionArthroplastyPeriprostheticHip replacementAcetabulum

Abstract

fetched live from OpenAlex

PURPOSE: The aim of the present study was to assess outcomes of using the push-through total femoral prothesis (PTTF) for revision total hip replacement with extreme bone loss. METHODS: 10 consecutive patients who received PTTF between 2012 and 2018 for revision hip arthroplasty were included in the study. Primary functional outcomes were assessed using Harris Hip Score (HHS), Toronto Extremity Salvage Score (TESS) and Musculoskeletal Tumor Society (MSTS) scores. Range of motion, complications, and ambulatory status were also recorded to assess secondary outcomes. RESULTS: 2 of 10 patients underwent surgery with PTTF for both knee and hip arthroplasty revision. The mean time between index surgery and PTTF was 15 years (3-32 yrs). Acetabular components were revised in 6 of 10 patients during PTTF application. After a mean follow-up of 5.9 years, hip dislocations occurred in 3 patients. All of the dislocated hips were ones with retained conventional non-constrained acetabular bearings. Patient satisfaction was high (MSTS: 67%, HHS: 61.2%, TESS 64.6%) despite high re-operation rate (40%) and minor postoperative problems. CONCLUSIONS: PTTF should be considered for hip and knee arthroplasty revision procedures in patients with an extreme bone defect. Consistent usage of constrained liners should be considered to avoid hip dislocation, which was our main problem following the procedure.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.608

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)0.0010.000

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 head0.035
GPT teacher head0.300
Teacher spread0.264 · 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 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
Published2024
Admission routes1
Has abstractyes

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