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Record W2464220504 · doi:10.1016/j.clon.2016.06.007

It's PRIMETIME. Postoperative Avoidance of Radiotherapy: Biomarker Selection of Women at Very Low Risk of Local Recurrence

2016· editorial· en· W2464220504 on OpenAlexaff
Cliona Kirwan, C.E. Coles, Judith M. Bliss, Lucy Kilburn, Louis Fox, Maggie C.U. Cheang, Claire Griffin, A. Francis, Anna M. Kirby, M. Ah-See, Ram Sharma, M. Mukesh, N. Twyman, Joseph Loane, Andrew Dodson, Elena Provenzano, I.H. Kunkler, John Yarnold, Paul D.P. Pharoah, Carlos Caldas, Hilary Stobart, Lesley Turner, Diego Megı́as

Bibliographic record

VenueClinical Oncology · 2016
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsNorthwood
FundersEuropean CommissionAcademy of Medical SciencesMedical Research CouncilNational Institute for Health and Care ResearchCancer Research UK
KeywordsMedicineBiomarkerSelection (genetic algorithm)OncologyRadiation therapyInternal medicineArtificial intelligence

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)0.017
metaresearch head score (Gemma)0.061
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.018
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.061
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0030.002
Science and technology studies0.0030.003
Scholarly communication0.0090.005
Open science0.0040.002
Research integrity0.0180.034
Insufficient payload (model declined to judge)0.0050.004

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 head0.015
GPT teacher head0.363
Teacher spread0.348 · 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
GenreEditorial

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

Citations85
Published2016
Admission routes1
Has abstractno

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