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Impact of internal mammary node inclusion in the radiation treatment volume on the outcomes of patients with breast cancer treated with locoregional radiation after six years of follow-up.

2011· article· en· W2560383530 on OpenAlexaff
Robert Olson, Ryan Woods, Jeffrey Lau, Caroline Speers, Andrea Lo, Scott Tyldesley, L. Weir

Bibliographic record

VenueJournal of Clinical Oncology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsMedicineBreast cancerRadiation therapyUnivariate analysisConfoundingInclusion and exclusion criteriaInternal medicineLymph nodeCancerOncologyMultivariate analysisPathology

Abstract

fetched live from OpenAlex

81 Background: There is ongoing controversy about radiotherapy (RT) to internal mammary nodes (IMNs). Proponents of IMN RT cite the survival benefit seen in postmastectomy RT trials that included IMNs. However, others point out that benefit cannot be definitively attributed to IMN inclusion, as other lymph node regions were included in the RT arms. The issue is important, as IMN RT potentially increases cardiac and respiratory morbidity. Methods: 2,413 women referred to a provincial RT program with newly diagnosed node positive, or T3/4N0 non-M1 invasive breast cancer, treated with a complete course of locoregional RT from 2001 to 2006, were retrospectively identified in a provincial database. IMN RT inclusion versus exclusion was determined through review of patient charts and RT treatment plans. Breast cancer-specific survival (BCSS), relapse-free survival (RFS), and overall survival (OS) were compared between the two groups using univariate and multivariable analyses. Results: Analyses were performed at a median follow-up of 6.2 years. 41.4% of the subjects received IMN RT. The 5-year BCSS for the IMN inclusion and exclusion group was 84.8% versus 82.9%, respectively (HR 0.93 [95% CI 0.76, 1.14]; p=.51); the 5-year RFS was 87.4% versus 86.9% (HR 0.993 [0.83, 1.19]; p=0.94); and the 5-year OS was 84.8% versus 82.9% (HR 0.84 [0.70, 1.01]; p=0.06). After controlling for potentially confounding variables, there was no significant difference in BCSS (HR 0.96 [0.78, 1.18], p=0.88), RFS (HR 1.02 [0.84, 1.22], p=0.87), or OS (HR 0.91 [0.76, 1.10]; p=0.35). Conclusions: After a median follow-up of 6.2 years, this population-based study shows no benefit from including IMNs in the locoregional RT volume after adjusting for other prognostic and treatment variables.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.029
GPT teacher head0.349
Teacher spread0.319 · 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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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