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Record W2088827279 · doi:10.1302/0301-620x.96b10.33470

The use of extracorporeally irradiated autografts in pelvic reconstruction following tumour resection

2014· article· en· W2088827279 on OpenAlexaboutno aff
Hazem Wafa, R. J. Grimer, Lee Jeys, A. Abudu, S. R. Carter, R. M. Tillman

Bibliographic record

VenueThe Bone & Joint Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsHemipelvectomyMedicinePelvisChondrosarcomaAmputationSarcomaSurgeryExtracorporeal

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the functional and oncological outcome of extracorporeally irradiated autografts used to reconstruct the pelvis after a P1/2 internal hemipelvectomy. The study included 18 patients with a primary malignant bone tumour of the pelvis. There were 13 males and five females with a mean age of 24.8 years (8 to 62). Of these, seven had an osteogenic sarcoma, six a Ewing's sarcoma, and five a chondrosarcoma. At a mean follow-up of 51.6 months (4 to 185), nine patients had died with metastatic disease while nine were free from disease. Local recurrence occurred in three patients all of whom eventually died of their disease. Deep infection occurred in three patients and required removal of their graft in two while the third underwent a hindquarter amputation for extensive flap necrosis. The mean Musculoskeletal Tumor Society functional score of the 16 patients who could be followed-up for at least 12 months was 77% (50 to 90). Those 15 patients who completed the Toronto Extremity Salvage Score questionnaire had a mean score of 71% (53 to 85). Extracorporeal irradiation and re-implantation of bone is a valid method of reconstruction after an internal hemipelvectomy. It has an acceptable morbidity and a functional outcome that compares favourably with other available reconstructive techniques.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.053
GPT teacher head0.268
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations72
Published2014
Admission routes1
Has abstractyes

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