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Record W3109412705 · doi:10.14740/gr1329

A Focused Review on Advances in Risk Stratification of Malignant Polyps

2020· review· en· W3109412705 on OpenAlexvenueno aff
Enoch Kuo, Kai Wang, Xiuli Liu

Bibliographic record

VenueGastroenterology Research · 2020
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSubmucosaColorectal cancerAdenomatous polypsMuscularis mucosaeLymph node metastasisLymph nodeRisk stratificationInternal medicineGastroenterologyPathologyCancerMetastasisColonoscopy

Abstract

fetched live from OpenAlex

Colorectal cancer is the third most common cancer in both men and women in the United States, with most cases arising from precursor adenomatous polyps. Colorectal malignant polyps are defined as cancerous polyps that consist of tumor cells invading through the muscularis mucosae into the underlying submucosa (pT1 tumor). It has been reported that approximately 0.5-8.3% of colorectal polyps are malignant polyps, and the potential for lymph node metastasis in these polyps ranges from 8.5% to 16.1%. Due to their clinical significance, recognition of malignant polyps is critical for clinical teams to make treatment decisions and establish appropriate surveillance schedules after local excision of the polyps. There is a rapidly developing interest in malignant polyps within the literature as a result of an increasing number of identifiable adverse histologic features and recent advancements in endoscopic treatment techniques. The purpose of this paper is to have a focused review of the recent histopathologic literature of malignant polyps.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.112
GPT teacher head0.440
Teacher spread0.328 · 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 designSystematic review
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

Citations18
Published2020
Admission routes1
Has abstractyes

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