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Record W3127009987 · doi:10.1038/s41416-020-01233-5

Single-dose intraoperative radiotherapy during lumpectomy for breast cancer: an innovative patient-centred treatment

2021· letter· en· W3127009987 on OpenAlexaff
Jayant S. Vaidya, Max Bulsara, Michael Baum, Jeffrey Tobias, Frederik Wenz, Samuele Massarut, Steffi Pigorsch, Michael Alvarado, Michael Douek, Christobel Saunders, Henrik Flyger, W. Eiermann, Chris Brew‐Graves, Norman Williams, Ingrid Potyka, Nicholas Roberts, Marcelle Bernstein, Douglas Brown, Elena Sperk, Siobhan Laws, Marc Sütterlin, Tammy Corica, Steinar Lundgren, Dennis R. Holmes, Lorenzo Vinante, Fernando A. Bozza, Montserrat Pazos, Magali Le Blanc‐Onfroy, Günther Gruber, Wojciech Polkowski, Konstantin J. Dedes, Marcus Niewald, Jens‐Uwe Blohmer, David R. McCready, Richard Hoefer, Pond R. Kelemen, Gloria Petralia, Mary Falzon, David Joseph

Bibliographic record

VenueBritish Journal of Cancer · 2021
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsPrincess Margaret Cancer Centre
FundersHealth Technology Assessment ProgrammeNational Institute for Health and Care ResearchUCL Hospitals Charitable Foundation
KeywordsLumpectomyMedicineRadiation therapyBreast cancerMastectomyIntraoperative radiotherapySurgeryCancerRadiologyInternal medicine

Abstract

fetched live from OpenAlex

In the randomised TARGIT-A trial, risk-adapted targeted intraoperative radiotherapy (TARGIT-IORT) during lumpectomy was non-inferior to whole-breast external beam radiotherapy, for local recurrence. In the long-term, no difference was found in any breast cancer outcome, whereas there were fewer deaths from non-breast-cancer causes. TARGIT-IORT should be included in pre-operative consultations with eligible patients.

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)0.002
metaresearch head score (Gemma)0.007
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.011
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0110.006
Insufficient payload (model declined to judge)0.0060.002

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 head0.016
GPT teacher head0.268
Teacher spread0.252 · 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
GenreCommentary

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

Citations21
Published2021
Admission routes1
Has abstractyes

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Same venueBritish Journal of CancerSame topicBreast Cancer Treatment StudiesFrench-language works237,207