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Record W1992919622 · doi:10.4021/gr304w

Small, Depressed-Type Early Colon Cancer Invading Shallow Submucosal Layer With Extensive Lymph Node Metastasis: A Case Report

2011· article· en· W1992919622 on OpenAlexvenueno aff
Yurika Kawamura

Bibliographic record

VenueGastroenterology Research · 2011
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineColorectal cancerLymphatic systemMetastasisLymph node metastasisInternal medicineIncidence (geometry)Lymph nodeBevacizumabCancerOncologyGastroenterologyLymphPathologyChemotherapy

Abstract

fetched live from OpenAlex

Early colorectal cancers are defined as invasive tumors that are limited to the mucosal layer or submucosal layer (SM), regardless of the presence or absence of lymph node (LN) metastasis. The reported incidence of LN metastasis of SM1 colon cancers is 0 - 5.9%, but the incidence in SM2 and SM3 colon cancers could be as high as 11.3 - 25.0%, and risk factors for LN metastasis include depth of SM invasion, growth patterns (polypoid or non-polypoid), histological sub-classification (moderate or poor differentiation) and regional lymphatic and vascular invasion. Among colorectal cancers with non-polypoid growth, the malignant potential is higher for depressed, than polypoid types, even for small tumors. Herein, we describe a patient with small, depressed-type early colon cancer with extensive LN metastasis and superficial SM invasion (pSM 450 µm). Six courses of chemotherapy with mFOLFOX6 and bevacizumab reduced the size of the LN metastases, thus eliciting a partial response (PR) according to the response evaluation criteria in solid tumors (RECIST).

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.192
GPT teacher head0.375
Teacher spread0.183 · 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 designCase report
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

Citations2
Published2011
Admission routes1
Has abstractyes

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