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Iliofemoral Arthrodesis and Pseudarthrosis: A Long-Term Functional Outcome Evaluation

2002· article· en· W2056970660 on OpenAlexaboutno aff
Bruno Fuchs, Mary I. O Connor, Kenton R. Kaufman, Denny J. Padgett, Franklin H. Sim

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2002
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePseudarthrosisArthrodesisSurgerySarcomaOrthopedic surgery

Abstract

fetched live from OpenAlex

Reconstruction after the resection of pelvic tumors is a major challenge. It depends on many factors such as age, activity level, type of tumor, its adjuvant treatment, and the extent of the disease. The purpose of the current study was to analyze the functional and oncologic outcomes of patients who had an iliofemoral arthrodesis after resection of a pelvic sarcoma. Between 1981 and 1999, 20 males and 12 females with a mean age of 39.9 years (range, 10-71 years) had an iliofemoral arthrodesis, either as a solid fusion or primary pseudarthrosis, at one institution. The functional outcome was evaluated using the Musculoskeletal Tumor Society and the Toronto Extremity Salvage scores. At a mean followup of 97 months (range, 14-226 months), 15 of 32 patients were alive, all without disease. The radiographic union rate was 86%. The mean overall Musculoskeletal Tumor Society and Toronto Extremity Salvage scores were 64% and 48%, respectively. Patients with a primary solid fusion did functionally better compared with patients who had pseudarthrosis (Toronto Extremity Salvage Score, 76%; Musculoskeletal Tumor Society Score, 71% versus Toronto Extremity Salvage Score, 52%; Musculoskeletal Tumor Society Score, 25%). Biomechanical analysis showed that the loss of motion in the hip is well-compensated. The authors conclude from this series that iliofemoral reconstruction after resection of a pelvic sarcoma provides acceptable and durable long-term results, not only from the oncologic, but also from the functional perspective.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.382
GPT teacher head0.489
Teacher spread0.106 · 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.

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

Citations93
Published2002
Admission routes1
Has abstractyes

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