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Record W2890585513 · doi:10.1016/j.jmir.2018.06.015

Raising the Bar: Developing Best Practice Guidelines for Breast Radiation Therapy Imaging

2018· article· en· W2890585513 on OpenAlexaff
Michele Cardoso, János Juhász, Nicole Harnett, Marcia Smoke

Bibliographic record

VenueJournal of medical imaging and radiation sciences · 2018
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoJuravinski Cancer Centre
Fundersnot available
KeywordsGuidelineBest practiceMedical physicsPaceImage-guided radiation therapyBreast imagingMedicineRadiation TherapistStakeholderBreast cancerRadiation therapyComputer scienceCancerRadiologyMammographyPublic relations

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)0.320
metaresearch head score (Gemma)0.633
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.320
Threshold uncertainty score0.839

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3200.633
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0120.007
Science and technology studies0.0130.011
Scholarly communication0.0260.022
Open science0.0180.024
Research integrity0.0370.056
Insufficient payload (model declined to judge)0.0070.004

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 head0.281
GPT teacher head0.564
Teacher spread0.283 · 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
Domainnot available
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
Published2018
Admission routes1
Has abstractno

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