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Record W4402619708 · doi:10.1111/jebm.12638

Evaluation of the reliability and usability of CARE‐Radiology: A descriptive‐analytic study

2024· article· en· W4402619708 on OpenAlexaff
Mengshu Wang, Xufei Luo, Janne Estill, Karen Spruyt, Ryo Kurokawa, Nav Persaud, Yasuteru Shimamura, Holly Raison, Paolo Niccolò Franco, Cesare Maino, Hussein Elkhayat, Rehab A. Galal, Daisuke Kimura, Shingo Omata, С. А. Рыжкин, Т. Р. Измайлов, Р. А. Баширов, Zhaoxiang Bian, Jinhui Tian, Junqiang Lei

Bibliographic record

VenueJournal of Evidence-Based Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUsabilityReliability (semiconductor)Descriptive researchPsychologyComputer scienceStatisticsMathematicsHuman–computer interactionPhysics

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Reporting · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptMetaresearch
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.083
metaresearch head score (Gemma)0.193
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.438

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.193
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.004
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.242
GPT teacher head0.446
Teacher spread0.204 · 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

Labeled directly by 2 models reading the full record.

Study designObservational
DomainReporting · Evaluation
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

Citations1
Published2024
Admission routes1
Has abstractyes

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