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Arthrodesis of the Shoulder after Tumor Resection

2005· article· en· W2024040447 on OpenAlexaboutno aff
Bruno Fuchs, Mary I. O Connor, Denny J. Padgett, Kenton R. Kaufman, Franklin H. Sim

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2005
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineArthrodesisResectionSurgeryOrthopedic surgeryPathology

Abstract

fetched live from OpenAlex

UNLABELLED: Functional outcomes of patients with arthrodesis after resection of a shoulder girdle neoplasm are only sparsely reported. Fusion of the shoulder can be done as a primary reconstruction or secondarily for salvage of a failed limb-sparing procedure. We retrospectively reviewed 21 patients at a mean followup of 11 years. In eight patients, arthrodesis was done as the primary reconstruction and in 13 patients as the secondary procedure. There were no local recurrences, and no patient had metastatic disease develop. The overall Toronto extremity salvage and Musculoskeletal Tumor Society scores were 81% (range, 46-97) and 23 points (range, 17-26), respectively. There was no difference with respect to function between patients who had their arthrodesis as a primary or secondary procedure. Eight of 21 patients (43%) had a complication that required major surgical intervention. Shoulder arthrodesis as a limb salvage procedure after tumor resection provides good function independent of whether the procedure is done primarily or secondarily. Because of the high complication rate, future efforts must be directed at surgical methods to decrease such complications. LEVEL OF EVIDENCE: Therapeutic study, Level IV (case series--no, or historical control group). See the Guidelines to Authors for a complete description of levels of evidence.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.092
GPT teacher head0.437
Teacher spread0.346 · 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 designCase report
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

Citations57
Published2005
Admission routes1
Has abstractyes

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