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Record W4415954564 · doi:10.3390/curroncol32110619

Adoption of Hypofractionated and Ultrahypofractionated Adjuvant Radiation Therapy for Breast Cancer Across Main and Community Centers Within a Single Healthcare System

2025· article· en· W4415954564 on OpenAlexvenueno aff
Leila T. Tchelebi, Ajay Kapur, Clary Evans

Bibliographic record

VenueCurrent Oncology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerAdjuvant radiotherapyAdjuvantStandardizationRadiation therapyHealth careHealthcare systemAdjuvant therapy

Abstract

fetched live from OpenAlex

Purpose/Objective(s): Adjuvant radiation therapy (RT) is an effective treatment in the management of patients with breast cancer. Evidence supports both standard fractionation and, more recently, moderate hypofractionation and ultra hypofractionation leading to a potential diversity of clinical practice. Whether or not physicians at main academic centers adopt hypofractionated regimens more readily than those working at community centers is not known. Practice patterns were analyzed within our large healthcare network comprising one main and eight community sites before and after 2020. Materials/Methods: Patients treated with adjuvant breast RT between 2017 and 2022 in our radiation oncology department were identified. Treatment techniques were evaluated: standard fractionation (25–28 fractions to 50–50.4 Gy), moderate hypofractionation (15–16 fractions to 40.05–42.56 Gy), and ultra hypofractionation (5 fractions of 26–30 Gy) for intact breast, partial breast, and chest wall cases. Use of each technique was compared between the main academic center (Main) versus eight community sites (Community) in two time periods, 2017–2019 and 2020–2022. Differences were assessed using z-ratios for the difference between independent proportions. Results: There was a statistically significant decrease in the use of standard fractionation for intact breast and chest wall cases from the early to the late period at both the community sites and the main center; however, a higher proportion of patients were treated with standard fractionation at the community sites versus the main center in the late period (7.8% community versus 2.0% main, p < 0.01 for intact breast and 80.7% community versus 37.4% main, p < 0.01 for chest wall). There was a statistically significant increase in the use of hypofractionation for intact breast and chest wall cases from the early to the late period at both the community sites and the main center; however, a higher proportion of patients were treated with hypofractionation at the main center versus the community sites during the late period (92.2% community versus 98.0% main, p < 0.01 for intact breast and 19.3% community versus 62.6% main, p < 0.01). Conclusions: The present study shows that recent trial evidence supporting the use of shorter RT treatments changed practice among providers more rapidly at our main academic center versus our community sites. The reasons for this difference are not known; however, standardization of treatment by implementation of an adjuvant RT treatment algorithm may facilitate uniform care among patients with breast cancer and we are investigating the impact of this approach.

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)0.004
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.050
GPT teacher head0.385
Teacher spread0.335 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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