MétaCan
Menu
Back to cohort
Record W4412752683 · doi:10.1002/path.6460

The importance of tumor microenvironment modulations in the progression of pancreatic intraductal papillary mucinous neoplasms<sup>†</sup>

2025· article· en· W4412752683 on OpenAlexaff
Antonio Pea, Claudio Luchini

Bibliographic record

VenueThe Journal of Pathology · 2025
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsPancreas Centre (Canada)
FundersFondazione Italiana per la ricerca sulle Malattie del Pancreas
KeywordsTumor microenvironmentPancreatic cancerDysplasiaPancreasIntraductal papillary mucinous neoplasmPancreatic Intraepithelial NeoplasiaPathologyTumor progressionMedicineCancer researchCancerImmune systemInternal medicineImmunologyPancreatic ductal adenocarcinoma

Abstract

fetched live from OpenAlex

Intraductal papillary mucinous neoplasms (IPMNs) of the pancreas have attracted substantial attention since they represent the most prevalent macroscopic precursor of pancreatic cancer. Most lesions show an epithelium with low-grade dysplasia and will remain indolent and unknown to the patient. Notably, a subgroup of IPMNs will progress to invasive cancer through a stepwise process characterized by the accumulation of specific genomic alterations and concomitant modifications of the tumor microenvironment (TME). The manuscript of Jamouss et al, recently published in The Journal of Pathology, expands the current knowledge on TME dynamics in IPMNs. The neoplastic progression of IPMNs is paralleled by a shift toward an immunosuppressive TME, with depletion of cytotoxic T cells, elevated expression of immune checkpoint molecules, including PD-L1 and VISTA, and increased density of macrophages. Overall, TME modifications are crucial in the progression of pancreatic IPMNs, calling for potential therapeutic strategies focused on TME modulations for cancer interception. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.025
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.319
Teacher spread0.304 · 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.

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of PathologySame topicPancreatic and Hepatic Oncology ResearchFrench-language works237,207