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Record W4395033826 · doi:10.1055/s-0044-1783486

Patients’ sentiments on artificial intelligence in endoscopy: A large-scale intercontinental opinion survey

2024· article· en· W4395033826 on OpenAlexaff
Jeroen de Groof, Omer F. Ahmad, Megan Engels, Sanne A. Hoogenboom, Nayantara Coelho–Prabhu, Honggang Yu, Michael Mwachiro, Sravanthi Parasa, Ricardo Mansilla, Junaid Mushtaq, Helmut Neumann, Shyam Thakkar, Michael F. Byrne, Jeanin E. van Hooft, Y. Tomonori

Bibliographic record

VenueEndoscopy · 2024
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsVancouver General Hospital
Fundersnot available
KeywordsMedicineScale (ratio)Artificial intelligenceEndoscopyData scienceRadiologyComputer scienceCartography

Abstract

fetched live from OpenAlex

Aims In recent years, the number of clinical studies evaluating artificial intelligence (AI) systems in endoscopy has increased. Authorities encourage integration of patients' thoughts in development of innovative medical interventions to allow their patient-friendly implementation. However, little is known about patient perception regarding AI in endoscopy.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.270
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.325
Teacher spread0.295 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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