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Record W4312100640 · doi:10.3390/curroncol30010015

Radiation in Early-Stage Breast Cancer: Moving beyond an All or Nothing Approach

2022· review· en· W4312100640 on OpenAlexvenueno aff
J. Purswani, Camille Hardy-Abeloos, Carmen A. Perez, Maryann Kwa, Manjeet Chadha, Naamit K. Gerber

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRadiation therapyBreast cancerNothingOncologyAdjuvant radiotherapyStage (stratigraphy)AdjuvantCancerInternal medicine

Abstract

fetched live from OpenAlex

Radiotherapy omission is increasingly considered for selected patients with early-stage breast cancer. However, with emerging data on the safety and efficacy of radiotherapy de-escalation with partial breast irradiation and accelerated treatment regimens for low-risk breast cancer, it is necessary to move beyond an all-or-nothing approach. Here, we review existing data for radiotherapy omission, including the use of age, tumor subtype, and multigene profiling assays for selecting low-risk patients for whom omission is a reasonable strategy. We review data for de-escalated radiotherapy, including partial breast irradiation and acceleration of treatment time, emphasizing these regimens' decreasing biological and financial toxicities. Lastly, we review evidence of omission of endocrine therapy. We emphasize ongoing research to define patient selection, treatment delivery, and toxicity outcomes for de-escalated adjuvant therapies better and highlight future directions.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.148
GPT teacher head0.437
Teacher spread0.289 · 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
Published2022
Admission routes1
Has abstractyes

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