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Record W3205551384 · doi:10.1038/s41591-021-01517-0

A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI

2021· letter· en· W3205551384 on OpenAlexafffund
Viknesh Sounderajah, Hutan Ashrafian, Sherri Rose, Nigam H. Shah, Marzyeh Ghassemi, Robert Golub, Charles E. Kahn, Andre Esteva, Alan Karthikesalingam, Bilal A. Mateen, Dale R. Webster, Dan Miléa, Daniel Shu Wei Ting, Darren Treanor, Dominic Cushnan, Dominic King, Duncan McPherson, Ben Glocker, Felix Greaves, Leanne Harling, Johan Ordish, Jérémie F. Cohen, Jonathan J Deeks, Mariska Leeflang, Matthew C. Diamond, Matthew D. F. McInnes, Melissa D. McCradden, Michael D. Abràmoff, Pasha Normahani, Sheraz R. Markar, Stephanie Chang, Xiaoxuan Liu, Susan Mallett, Shravya Shetty, Alastair K. Denniston, Gary S. Collins, David Moher, Penny Whiting, Patrick M. Bossuyt, Ara Darzi

Bibliographic record

VenueNature Medicine · 2021
Typeletter
Languageen
FieldMedicine
TopicArtificial Intelligence in Healthcare and Education
Canadian institutionsOttawa HospitalHospital for Sick ChildrenUniversity of Ottawa
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNIHR Oxford Biomedical Research CentreNIHR Imperial Biomedical Research CentreMassachusetts General HospitalUK Research and InnovationAssistance publique-Hôpitaux de ParisInstitut National de la Santé et de la Recherche MédicaleSingapore Eye Research InstituteUniversity of OttawaImperial College LondonLinköpings UniversitetWellcome TrustUniversity College LondonCancer Research UKOxford University Hospitals NHS Foundation TrustAmsterdam University Medical CentersNational Institutes of HealthUniversity Hospitals Birmingham NHS Foundation TrustNuclear Power Institute of ChinaUniversiteit van AmsterdamUniversité de ParisOttawa Hospital Research InstituteUniversity of OxfordUniversity of LeedsNational Institute for Health and Care ExcellenceMassachusetts Institute of TechnologyUniversity of PennsylvaniaNational Institute for Health and Care Research
KeywordsTest (biology)Diagnostic accuracyDiagnostic testArtificial intelligenceComputer scienceQuality (philosophy)Quality assessmentMedical physicsMachine learningExternal quality assessmentMedicinePathologyBiologyInternal medicinePediatrics

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)0.143
metaresearch head score (Gemma)0.449
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.857
Threshold uncertainty score0.757

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1430.449
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.005
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0030.004
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0040.002

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 head0.334
GPT teacher head0.548
Teacher spread0.215 · 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 designTheoretical or conceptual
DomainMethods
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

Citations242
Published2021
Admission routes2
Has abstractno

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