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Record W4304127925 · doi:10.1016/j.radonc.2022.10.004

Pan-Canadian consensus recommendations for proton beam therapy access in Canada

2022· article· en· W4304127925 on OpenAlexaffabout
Gunita Mitera, Derek S. Tsang, Boyd McCurdy, Karen Goddard, Annie Ébacher, Tim Craig, Jonathan Greenland, Staci Kentish, Rashmi Koul, Natalie Logie, Mélanie Morneau, Andra Morrison, Larry Pan, Jason Pantarotto, Sophie Foxcroft, Jonathan Sussman, R. Houston Thompson, Scott Tyldesley, Philip Wright, Sarah Hicks, Erika Brown, Samir Patel

Bibliographic record

VenueRadiotherapy and Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsUniversity of AlbertaHorizon Health NetworkCanadian Agency for Drugs and Technologies in HealthHealth PEISaskatchewan Cancer AgencyUniversity Health NetworkCancerCare ManitobaUniversité de SherbrookeSt. John’s Health Sciences CentreCancer Care OntarioAlberta Health ServicesMinistry of Health and Social ServicesThe Canadian Association of Professional Academic Librarians
Fundersnot available
KeywordsReferralMedicineJurisdictionFamily medicineQuality assurancePolitical science

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)0.020
metaresearch head score (Gemma)0.042
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: Empirical · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score0.782

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.005
Bibliometrics0.0070.008
Science and technology studies0.0070.003
Scholarly communication0.0080.002
Open science0.0100.004
Research integrity0.0100.010
Insufficient payload (model declined to judge)0.0180.002

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 head0.042
GPT teacher head0.348
Teacher spread0.306 · 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
GenreEmpirical

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

Citations11
Published2022
Admission routes2
Has abstractno

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