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Record W2316425662 · doi:10.1155/2002/354181

Motion – Screening and Surveillance of Barrett’s Epithelium Is Practical and Cost Effective: Arguments against the Motion

2002· article· en· W2316425662 on OpenAlexaffvenue
Naoki Chiba

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2002
Typearticle
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMotion (physics)EpitheliumComputer scienceComputer visionArtificial intelligenceMedicinePathology

Abstract

fetched live from OpenAlex

The issue of whether to screen individuals for Barrett's esophagus (BE) to prevent esophageal adenocarcinoma (EAC) is highly controversial. Important considerations are that BE is not highly prevalent in the general population and that not many patients with BE develop or die from EAC. Studies that suggest an improved prognosis from surveillance programs are susceptible to lead-time bias. Most of the principles for effective screening, as outlined by the World Health Organization, are not met by endoscopic screening and surveillance protocols. The diagnosis of BE (and dysplasia) is often unclear. Most patients with BE are not identified by screening, and few deaths would be prevented by surveillance. A decision analysis found that the most cost effective screening protocol would be every five years, but the costs associated with prolongation of life are very high, even if a group at high risk for EAC could be identified.

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 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)0.038
metaresearch head score (Gemma)0.130
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.038
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.130
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.014
Scholarly communication0.0040.010
Open science0.0020.003
Research integrity0.0120.011
Insufficient payload (model declined to judge)0.0120.002

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 head0.022
GPT teacher head0.286
Teacher spread0.264 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations5
Published2002
Admission routes2
Has abstractyes

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