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Record W4319987869 · doi:10.21608/ejhm.2023.280210

Results of Using Modular Mega Prosthesis after Proximal Femoral Tumor Resection

2023· article· en· W4319987869 on OpenAlexaboutno aff
Wael Mansour Wafa, Yasser Youssef Abed, Sallam Ibrahim Fawzy

Bibliographic record

VenueThe Egyptian Journal of Hospital Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMega-ResectionProsthesisModular designSurgeryGeneral surgeryRadiology

Abstract

fetched live from OpenAlex

Background: Proximal femoral replacement (PFR) is a commonly performed procedure to restore extensive bone defects for different indications with variable reported outcomes. Objective: This retrospective study aimed to assess the functional outcomes and complication rates of PFR with MUTARS or Hipokrat modular femoral mega prosthesis after oncological resections and to highlight the overall patient, limb, and implant survivorship. Patients and methods: A total of 18 patients had PFR after oncological resection. 14 patients had bipolar hemiarthroplasty (BHA) and 4 patients had total hip arthroplasty (THA). At the final follow-up, the patient’s functional outcome was assessed by Muscloskeletal Tumor Society score (MSTS) and Toronto Extremity Salvage Score (TESS). Complications were recorded and classified according to the Henderson classification. Results: The mean follow-up was 47.78 months (14-103 months). The mean MSTS and TESS score was 65.7 (range 23-97%) and 81 (range 56-98) respectively. Overall limb, implant, and 5-year patient survival were 94%, 94%, and 66% respectively. The overall complication rate was 39%; 11% instability, 17% periprosthetic fracture, 5.6% infection, and 5.6% local tumor recurrence. Conclusion: PFR is a valid option for reconstruction of huge bone loss after oncological resection of the proximal femur with acceptable longevity, functional outcome, and complication rate with better BHA over THA reconstructive option for stability issues.

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)0.001
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 score0.436
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.022
GPT teacher head0.276
Teacher spread0.255 · 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
Published2023
Admission routes1
Has abstractyes

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