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Functional Outcome of Endoprosthetic Proximal Femoral Replacement

2004· article· en· W2004145114 on OpenAlexaffabout
Christian M. Ogilvie, Jay S. Wunder, Peter C. Ferguson, Anthony M. Griffin, Robert S. Bell

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2004
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineFemurSurgeryGreater trochanterProsthesisSoft tissueHarris Hip ScoreOrthopedic surgeryHip replacementSports medicineArthroplastyPhysical therapy

Abstract

fetched live from OpenAlex

Endoprosthetic proximal femur replacement is a well-accepted method for treatment of primary bone tumors; however the functional results of treatment are not well documented. To evaluate functional outcomes, we recorded the Toronto Extremity Salvage Score and Musculoskeletal Tumor Society 1987 and 1993 scores in 29 patients, and also recorded Musculoskeletal Tumor Society scores alone in four more patients treated with endoprosthetic proximal femur replacement. The mean followup was 3 years. Twelve patients had a total hip endoprosthetic proximal femur replacement, and 21 had a bipolar hip endoprosthetic proximal femur replacement. In nine patients, the greater trochanter was attached to the femoral prosthesis. Sixteen patients had an abductor soft tissue repair, and in eight patients, no abductor repair was possible. The mean Musculoskeletal Tumor Society 1987 score was 23.2 +/- 4.1 points of 35 points. The mean Musculoskeletal Tumor Society 1993 score was 67.7 +/- 12.0%. The Toronto Extremity Salvage Score mean was 76.2 +/- 16.2 points of 100 points. Functional scores did not differ significantly between abductor repair types. There was a trend toward less disability in patients with abductor soft tissue repair compared with patients with no abductor repair. Functional results were similar in patients receiving bipolar and total hip replacements.

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)0.004
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 score0.028
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.151
GPT teacher head0.435
Teacher spread0.284 · 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

Citations58
Published2004
Admission routes2
Has abstractyes

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