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Record W4395052736 · doi:10.1055/s-0044-1782803

Computer-Aided Diagnosis for the Resect and Discard Strategy for Colorectal Polyps: A Systematic Review and Meta-Analysis

2024· review· en· W4395052736 on OpenAlexaff
Tommy Rizkala, Cesare Hassan, Marco Spadaccini, G. Antonelli, Antonio Facciorusso, Britt B. S. L. Houwen, E. Dekker, James Weiquan Li, Emanuele Rondonotti, Kareem Khalaf, Masashi Misawa, María Menini, Alessandro Schilirò, Harsh K. Patel, Oliver Pech, Frederik Simon Bäumer, Alessandro Fugazza, Roberta Maselli, Kevin Douglas, Prateek Sharma, Alessandro Repici

Bibliographic record

VenueEndoscopy · 2024
Typereview
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineMeta-analysisGeneral surgeryColorectal PolypColonoscopyColorectal cancerPathologyInternal medicine

Abstract

fetched live from OpenAlex

Aims According to the Resect and Discard strategy, endoscopists can replace post-polypectomy pathology with real-time prediction (optical diagnosis) of polyp histology during colonoscopy. This strategy is only applicable to small polyps≤5mm and if the endoscopist prediction was made with high confidence. The variability in real-time optical diagnosis among different endoscopists can be standardized by the high accuracy expected from computer-aided diagnosis systems (CADx). The aim of this meta-analysis is to provide a preliminary estimate of the accuracy of CADx and to assess its effect in clinical practice.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.778
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.003
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 head0.145
GPT teacher head0.417
Teacher spread0.272 · 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
GenreReview

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

Citations4
Published2024
Admission routes1
Has abstractyes

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