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Record W7117104805 · doi:10.1056/aioa2500522

International Retrospective Observational Study of Continual Learning for AI on Endotracheal Tube Placement from Chest Radiographs

2025· article· en· W7117104805 on OpenAlexaff
Emma Chen, Agustina Saenz, Oishi Banerjee, Henrik Marklund, Xiaoman Zhang, Shreya Johri, Luyang Luo, Subathra Adithan, Kay Wu, Siddhant Dogra, Vijay Janapa Reddi, Dominic Buensalido, Helen Kavnoudias, Roman Kloeckner, Lukas Müller, Emmanuel Salinas-Miranda, Maria José Veloza Vega, Johannes Kolck, Tobias Penzkofer, Daiju Ueda, Shannon L. Walston, Armin Quispe Cornejo, Michele Salvagno, Christopher Lee, Jonathan Fournier, Rosa Castillo, Cibele Luna, Tara Bahramipour, Amanda R. Zuback, Rickmer Braren, Yutthaphan Wannasopha, Piyapong Khumrin, Desmond Lim Shi Wei Wei, James Thomas Patrick Decourcy Hallinan, Zhicheng Jiao, Thomas Yi, Juana María Plasencia Martinez, Nuria Isabel Casado Alarcon, Franz A. Fellner, Julian F. Niedermair, Derek Wu, Dongkeun Kim, Johannes Haubold, Lars Heiliger, Daniel Pérez-Chada, Pablo Pratesi, Ryan Cummings, Narges Razavian, Anastasia Oikonomou, William T. Tran, Thomas Küstner, Saif Afat, Adrienne N. Dula, Justin F. Rousseau, Franko Hržić, Michael Fuchsjäger, Pranav Rajpurkar

Bibliographic record

VenueNEJM AI · 2025
Typearticle
Languageen
FieldMedicine
TopicArtificial Intelligence in Healthcare and Education
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsObservational studyRadiographyRetrospective cohort studyEndotracheal tubeMEDLINE

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.166
GPT teacher head0.455
Teacher spread0.289 · 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 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

Citations2
Published2025
Admission routes1
Has abstractno

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Same venueNEJM AISame topicArtificial Intelligence in Healthcare and EducationFrench-language works237,207