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Preoperative Radiotherapy is Effective in the Treatment of Fibromatosis

2003· article· en· W2069736720 on OpenAlexaff
F. J. O Dea, Jay S. Wunder, Robert S. Bell, Anthony M. Griffin, Charles Catton, B. O Sullivan

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2003
Typearticle
Languageen
FieldMedicine
TopicSoft tissue tumor case studies
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineAggressive fibromatosisFibromatosisRadiation therapySoft tissueSoft tissue sarcomaSarcomaSurgeryComplicationRadiologyPathology

Abstract

fetched live from OpenAlex

The use of preoperative radiation is well-established for soft tissue sarcoma, but its use in fibromatosis is not well-characterized. The purpose of this study was to examine the impact of preoperative radiotherapy on the local control of fibromatosis and to assess treatment-related morbidity in this setting. In particular we assessed complication rates in comparison with soft tissue sarcoma treatment. All patients with fibromatosis referred to this unit who received preoperative radiotherapy (50 Gy in 25 fractions) from 1988 to 2000 and who had at least 2 years of followup were included in this study. The rate of recurrence in this group was ascertained. Similarly constructed datasets from all patients with soft tissue sarcomas of the extremities who received preoperative radiation from 1986 to 1997 also were analyzed. The rates of complications in the two groups were compared. Fifty-eight patients were treated with preoperative radiation for fibromatosis and the median followup was 69 months. There were 11 local recurrences (19%). Major wound complications manifested in two patients (3.4%). Wound-related complications arose in 89 of 265 patients with soft tissue sarcomas (33.5%). There was a significant difference in the rate of major wound complications observed in the two groups. The use of radiotherapy before surgery is effective in the combined treatment of fibromatosis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

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

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 head0.092
GPT teacher head0.465
Teacher spread0.374 · 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

Citations28
Published2003
Admission routes1
Has abstractyes

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