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Record W4386843353 · doi:10.1177/08465371231192391

Canadian Association of Radiologists Breast Disease Imaging Referral Guideline

2023· article· en· W4386843353 on OpenAlexafffundabout
Candyce Hamel, Barb Avard, Carolyn Flegg, Vivianne Freitas, Connie Hapgood, Supriya Kulkarni, Pam Lenkov, Matthew Seidler

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsSunnybrook HospitalWomen's College HospitalPrincess Margaret Cancer CentreCentre Hospitalier de l’Université de MontréalSaskatoon City HospitalNorth York General Hospital
FundersCanadian Medical Association
KeywordsMedicineGuidelineGrading (engineering)ReferralBreast imagingMedical physicsAppropriate Use CriteriaMEDLINEAsymptomaticAppropriateness criteriaFamily medicineMammographyRadiologyBreast cancerPathologyInternal medicineCancer

Abstract

fetched live from OpenAlex

The Canadian Association of Radiologists (CAR) Breast Disease Expert Panel consists of breast imaging radiologists, a high-risk breast clinician, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 20 clinical/diagnostic scenarios, a systematic rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of these clinical/diagnostic scenarios. Recommendations from 30 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) for guidelines framework were used to develop 69 recommendation statements across the 20 scenarios. This guideline presents the methods of development and the recommendations for referring asymptomatic individuals, symptomatic patients, and other scenarios requiring imaging of the breast.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.321
Teacher spread0.282 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations14
Published2023
Admission routes3
Has abstractyes

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