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Record W4247010434 · doi:10.1016/j.radonc.2015.12.027

ESTRO consensus guideline on target volume delineation for elective radiation therapy of early stage breast cancer, version 1.1

2016· letter· en· W4247010434 on OpenAlexaff
Birgitte Vrou Offersen, Liesbeth Boersma, Carine Kirkove, S. Hol, Marianne Aznar, Albert Biete Solá, Youlia Kirova, Jean‐Philippe Pignol, Vincent Remouchamps, Karolien Verhoeven, Caroline Weltens, Meritxell Arenas, Dorota Gabryś, Neil Kopek, Mechthild Krause, Dan Lundstedt, Tanja Marinko, Á. Montero, John Yarnold, Philip Poortmans

Bibliographic record

VenueRadiotherapy and Oncology · 2016
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsGuidelineStage (stratigraphy)MedicineBreast cancerRadiation therapyMedical physicsCancerRadiologyOncologyInternal medicinePathologyBiology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.010
metaresearch head score (Gemma)0.045
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: Other · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0050.002
Research integrity0.0230.016
Insufficient payload (model declined to judge)0.0110.013

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 head0.012
GPT teacher head0.302
Teacher spread0.290 · 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
GenreOther

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

Citations241
Published2016
Admission routes1
Has abstractno

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