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Record W4402170515 · doi:10.1016/j.ijrobp.2024.08.037

Early-Stage Breast Cancer: A Critical Review of Current and Emerging Practice

2024· review· en· W4402170515 on OpenAlexafffund
Danielle Rodin, Rachel Glicksman, Jaroslaw T. Hepel, Kathryn Huber, Youlia Kirova, Pierre Loap, Eileen Rakovitch

Bibliographic record

VenueInternational Journal of Radiation Oncology*Biology*Physics · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentrePrincess Margaret Cancer CentreUniversity of Toronto
FundersCanadian Cancer SocietyAmerican College of Radiation OncologyAmerican Society for Radiation Oncology
KeywordsStage (stratigraphy)Breast cancerCurrent (fluid)CancerMedicineOncologyInternal medicineEngineeringBiology

Abstract

fetched live from OpenAlex

Breast-conserving surgery followed by adjuvant radiation to reduce the risk of ipsilateral breast tumor recurrence is the mainstay of treatment for early-stage breast cancer (ESBC). However, improved understanding of the heterogeneity of the clinical and molecular characteristics of ESBC has led to greater efforts to personalize approaches to treatment. Furthermore, advances in the understanding of the radiobiology of breast cancer have led to several practice-changing trials on the effectiveness and tolerability of moderate and ultrahypofractionated radiation. Here, we review the recent evidence and ongoing research in the radiotherapeutic management of ESBC, including the use of boost for high-risk disease and opportunities for accelerated fractionation, partial breast irradiation, and radiation omission for low-risk disease. We also discuss how molecular profiling can inform decision-making and new opportunities for primary radiation therapy and reirradiation.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.039
GPT teacher head0.433
Teacher spread0.395 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Radiation Oncology*Biology*PhysicsSame topicBreast Cancer Treatment StudiesFrench-language works237,207