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Record W4388517429 · doi:10.1055/s-0043-1765353

Real-Time Computer-Aided Detection of Colorectal Neoplasia during Colonoscopy: Systematic review and meta-analysis

2023· article· en· W4388517429 on OpenAlexaff
M. Spadaccini, C Hassan, D. M. Alessandro, F. Antonio, T. Georgios, T. Konstantinos, Anna Maria Di Giulio, K. Kareem, R. Tommy, B. Michael, O. V. Per, F. Farid, F. Alessandro, R. Emanuele, Richmond Jeremy, B. Raf, E. Dekker, F Michal, José L. Rodrigo, S. Prateek, Kevin Douglas, R. Alessandro

Bibliographic record

VenueEndoscopy · 2023
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsTed Rogers Centre for Heart Research
Fundersnot available
KeywordsMedicineColonoscopyMeta-analysisColorectal cancerMedical physicsInternal medicineCancer

Abstract

fetched live from OpenAlex

Aims Artificial intelligence by computer-aided Detection (CADe) of colorectal neoplasia during colonoscopy may increase adenoma detection rates (ADR). We quantified benefit and harms of CADe in randomized trials.

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: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
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 head0.021
GPT teacher head0.286
Teacher spread0.265 · 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 designMeta-analysis
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

Citations3
Published2023
Admission routes1
Has abstractyes

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