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Record W4410549384 · doi:10.1016/s0167-8140(25)03225-6

4219 Harmonizing oncology data: a pan-Canadian strategy for radiotherapy data standards

2025· article· en· W4410549384 on OpenAlexaboutno aff
Amanda Caissie, Monique Ashe, Jean‐Pierre Bissonnette, Erika Brown, R. Chmielewski, Carol-Anne Davis, Caitlin Gillan, Eric Gutierrez, Nareesa Ishmail, K.R. MacKenzie, Brian Liszewski, Michelle Nielsen, Jason Pantarotto, Teri Stuckless, Kathleen Surry

Bibliographic record

VenueRadiotherapy and Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsnot available
Fundersnot available
KeywordsRadiation oncologyRadiation therapyOncologyMedical physicsMedicineInternal medicine

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)0.167
metaresearch head score (Gemma)0.238
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.939

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1670.238
Meta-epidemiology (narrow)0.0010.003
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0170.027
Science and technology studies0.0100.007
Scholarly communication0.0220.008
Open science0.0190.014
Research integrity0.0100.016
Insufficient payload (model declined to judge)0.0140.005

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 head0.098
GPT teacher head0.458
Teacher spread0.359 · 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.

Study designNot applicable
DomainMethods
GenreMethods

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 abstractno

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