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Record W4416737940 · doi:10.1007/s00247-025-06386-0

AI implementation in pediatric radiology for patient safety: a multi-society statement from the ACR, ESPR, SPR, SLARP, AOSPR, SPIN

2025· article· en· W4416737940 on OpenAlexafffund
Susan C. Shelmerdine, Jaishree Naidoo, Brendan S. Kelly, Lene Bjerke Laborie, Seema Toso, Tugba Akinci D’Antonoli, Owen J. Arthurs, Steven L. Blumer, Pierluigi Ciet, Maria Beatrice Damasio, Andréa S. Doria, Saira Haque, Mai‐Lan Ho, Thierry A.G.M. Huisman, Aparna Joshi, Jeevesh Kapur, Kshitij Mankad, Amaka C Offiah, Hansel J. Otero, Erika Pace, Kushaljit Singh Sodhi, Sebastian Tschauner, Carlos F. Ugas-Charcape, Dhananjaya K. Vamyanmane, Rick R. van Rijn, Diana Veiga-Canuto, Matthias Wagner, Evan J. Zucker, Marla B. K. Sammer

Bibliographic record

VenuePediatric Radiology · 2025
Typearticle
Languageen
FieldMedicine
TopicArtificial Intelligence in Healthcare and Education
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekTerry Fox FoundationJapanese Association of Cardiac RehabilitationEuropean Maritime and Fisheries FundRoyal Marsden Cancer CharityNational Institute for Health and Care ResearchRadiological Society of North America
KeywordsWorkgroupTransparency (behavior)Patient safetyPosition statementStakeholderInclusion (mineral)Position paperSoftware deployment

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)0.139
metaresearch head score (Gemma)0.163
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: Editorial · Consensus signal: none
Teacher disagreement score0.139
Threshold uncertainty score0.733

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1390.163
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0050.006
Scholarly communication0.0100.006
Open science0.0070.010
Research integrity0.0150.022
Insufficient payload (model declined to judge)0.0050.003

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 head0.061
GPT teacher head0.439
Teacher spread0.379 · 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
GenreEditorial

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
Published2025
Admission routes2
Has abstractno

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