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Record W3105468253 · doi:10.1093/jcag/gwaa036

Clinical Importance of Magnification in the Assessment of Colorectal Lesions

2020· article· en· W3105468253 on OpenAlexaff
Robert Bechara, Paul W. Manley

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsQueen's University
Fundersnot available
KeywordsMagnificationMedicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

A 56-year-old man had a 1.5 cm Paris 1s+2c rectal lesion detected on fecal immunochemical test-positive colonoscopy. Examining the lesion using optical enhancement mode 1 (OE-1), which utilizes wavelengths 415 nm and 540 nm, highlights the microvasculature. This revealed a NICE III lesion, indicating cancer (Figure 1) (1). Rectal Lesion. White light images of lesion: (A) distant view, (B) near view, (C) low magnification. OE-1 images of lesion: (D) distant view, (E) low magnification, (F) medium magnification. Rectal Lesion. White light images of lesion: (A) distant view, (B) near view, (C) low magnification. OE-1 images of lesion: (D) distant view, (E) low magnification, (F) medium magnification. The NICE classification is advantageous in its simplicity and the fact that magnification is unnecessary. However, a drawback is that neoplastic lesions are only classified into two categories: NICE II (adenoma) and NICE III (invasive cancer). To address this limitation, the Japan NBI Expert Team (JNET) created the JNET classification where NICE II lesions are subclassified using magnification: JNET 2A (low-grade adenoma) and JNET 2B (high-grade dysplasia or superficially invasive cancer) (2). Under magnification, the lesion of interest was classified as JNET 2B, which suggests superficial neoplasia that is potentially resectable endoscopically. Considering the central depression along with microvasculature findings, the lesion was staged as a rectal cancer. MRI staged the lesion as a T1-T2, and CT was negative for any metastatic disease. An endoscopic submucosal dissection (ESD) was completed and a curative R0 resection was achieved with final pathology of a well-differentiated adenocarcinoma with superficial submucosal invasion (<_ 1000 µm) without tumor budding or lymphovascular invasion (Figure 2). Resection and pathology: (A) submucosal injection of lesion; (B) ESD: muscularis propria on the left and submucosa/mucosa to the right; (C) defect post-ESD; (D) pined gross specimen; (E) 20× with hematoxylin phloxine saffron (HPS) stain: tubular adenoma with high-grade dysplasia and focus of superficial adenocarcinoma; (F) 40× HPS stain: focus of submucosal cancer with penetration depth of 400 um. Resection and pathology: (A) submucosal injection of lesion; (B) ESD: muscularis propria on the left and submucosa/mucosa to the right; (C) defect post-ESD; (D) pined gross specimen; (E) 20× with hematoxylin phloxine saffron (HPS) stain: tubular adenoma with high-grade dysplasia and focus of superficial adenocarcinoma; (F) 40× HPS stain: focus of submucosal cancer with penetration depth of 400 um.

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)0.003
metaresearch head score (Gemma)0.025
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 score0.004
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
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 head0.030
GPT teacher head0.319
Teacher spread0.290 · 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".

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Citations0
Published2020
Admission routes1
Has abstractno

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