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The use of moderately hypofractionated post-operative radiation therapy for breast cancer in clinical practice: A critical review

2020· review· en· W3080704689 on OpenAlexaff
Gustavo Nader Marta, Charlotte E. Coles, Orit Kaidar‐Person, Icro Meattini, Tarek Hijal, Yvonne Zissiadis, Jean‐Philippe Pignol, Duvern Ramiah, Alice Y. Ho, Skye Hung‐Chun Cheng, Gemma Sancho, Birgitte Vrou Offersen, Philip Poortmans

Bibliographic record

VenueCritical Reviews in Oncology/Hematology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsDalhousie UniversityMcGill University Health Centre
FundersNational Institute for Health and Care Research
KeywordsMedicineBreast cancerRadiation therapyClinical PracticeClinical trialOncologyCancerMedical physicsRadiologyInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

Post-operative radiation therapy (RT) reduces loco-regional recurrence rates and mortality in most patients with non-metastatic breast cancer. The aim of this critical review is to provide an overview of the applicability of moderately hypofractionated RT for breast cancer patients, focusing on factors influencing clinical decision-making. An international group of radiation oncologists agreed to assess, integrate, and interpret the existing evidence into a practical report to guide clinicians in their daily management of breast cancer patients. We conclude that moderately hypofractionated RT to the breast, chest wall (with/without breast reconstruction), and regional lymph nodes is at least as safe and effective as conventionally fractionated regimens and could be considered as the treatment option for the vast majority of the patients.For those who are still concerned about its generalised application, we recommend participating in ongoing trials comparing moderately hypofractionated RT to conventionally fractionated RT for breast cancer patients in some clinical circumstances.

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)0.002
metaresearch head score (Gemma)0.048
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.048
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.180
GPT teacher head0.533
Teacher spread0.354 · 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.

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

Citations55
Published2020
Admission routes1
Has abstractyes

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