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Record W2730119757 · doi:10.1038/bjc.2017.198

Chemotherapy with radiotherapy influences time-to-development of radiation-induced sarcomas: a multicenter study

2017· article· en· W2730119757 on OpenAlexaff
Alison Y. Zhang, Ian Judson, Charlotte Benson, Jay S. Wunder, Isabelle Ray‐Coquard, R. J. Grimer, R. Quek, Ee-Hwee Wong, Aisha Miah, Peter C. Ferguson, Armelle Dufresne, Jonathan Teh, Martin R. Stockler, Martin H.N. Tattersall

Bibliographic record

VenueBritish Journal of Cancer · 2017
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsUniversity of TorontoMount Sinai Hospital
FundersEuropean Society for Medical Oncology
KeywordsRadiation therapyMedicineChemotherapyMulticenter studyOncologyPathologyRadiologyInternal medicineRandomized controlled trial

Abstract

fetched live from OpenAlex

BACKGROUND: An increasing number and proportion of cancer patients with apparently localised disease are treated with chemotherapy and radiation therapy in contemporary oncology practice. In a pilot study of radiation-induced sarcoma (RIS) patients, we demonstrated that chemotherapy was associated with a reduced time to development of RIS. We now present a multi-centre collaborative study to validate this association. METHODS: This was a retrospective cohort study of RIS cases across five large international sarcoma centres between 1 January 2000 to 31 December 2014. The primary endpoint was time to development of RIS. RESULTS: We identified 419 patients with RIS. Chemotherapy for the first malignancy was associated with a shorter time to RIS development (HR 1.37; 95% CI: 1.08-1.72; P=0.009). In the multi-variable model, older age (HR 2.11; 95% CI 1.83-2.43; P<0.001) and chemotherapy for the first malignancy (HR 1.61; 95% CI 1.26-2.05; P<0·001) were independently associated with a shorter time to RIS. Anthracyclines and alkylating agents significantly contribute to the effect. CONCLUSIONS: This study confirms an association between chemotherapy given for the first malignancy and a shorter time to development of RIS.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.018
GPT teacher head0.323
Teacher spread0.305 · 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

Citations24
Published2017
Admission routes1
Has abstractyes

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