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Pathologic characteristics of pancreatic cancer in Shanghai urban area:preliminary analysis of 350 cases

2012· article· en· W2354968404 on OpenAlexaff
Yuan Ji, Ming Shen, Xiao Jin, Zhu Ming, Hong Wang, Quan Xing Ni, Wei Zhang, Jing Wang, Lu Sun, Herbert Yu, Harvey A. Risch, Yu Tang Gao

Bibliographic record

VenueTumori · 2012
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsMedicinePancreatic cancerLymphovascular invasionPancreasPerineural invasionStage (stratigraphy)AdenocarcinomaCancerPopulationPathologyInternal medicineMetastasis

Abstract

fetched live from OpenAlex

Objective:To investigate the pathologic characteristics and differential diagnosis of pancreatic cancer among residents in Shanghai urban area.Methods:A population-based large case-control study was performed in patients with pancreatic cancer between December 2006 and January 2011 in Shanghai urban area.The pathologic slides from 387 cases of pancreatic cancer were collected and reviewed by five experienced pathologists.The pathologic diagnosis was determined according to WHO Classification of Tumors of the Digestive System,Fourth Edition(2010).Results:Three hundred and fifty cases(90.4%) were confirmed as pancreatic cancer.Most of the pathologic slides were obtained from resected tissues of pancreatic cancer(311/350,88.9%).The average diameter of the tumor was 4 cm,and 56.6%(n = 198) of the tumors were located in the head of pancreas.The ductal adenocarcinoma was the most common histologic subtype(n = 311,89.0%),followed by pancreatic neuroendocrine tumors(n = 15,4.3%).The most common histologic grades were Ⅱ/Ⅲ(n = 252).According to TNM staging system,40 cases had stage ⅠA,209 cases had stages ⅠB/ⅡA,and 80 cases had stage ⅡB.The perineural invasion was identified in 175 cases,and the lymphovascular and/or venous emboli were identified in 56 cases.Only one case was misdiagnosed.Conclusion:The understanding of pathology of pancreatic cancer in Shanghai urban area was improved through this pathologic review.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.054
GPT teacher head0.356
Teacher spread0.303 · 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.

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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