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Record W4322619771 · doi:10.3390/biomedicines11030731

A Retrospective Study on the Role of Metformin in Colorectal Cancer Liver Metastases

2023· article· en· W4322619771 on OpenAlexafffund
Miran Rada, Lucyna Krzywoń, Stephanie Petrillo, Anthoula Lazaris, Peter Metrakos

Bibliographic record

VenueBiomedicines · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsMcGill University Health Centre
FundersMcGill University Health CentreMcGill University
KeywordsMetforminMedicineRetrospective cohort studyInternal medicineColorectal cancerCancerOncologyChemotherapyIncidence (geometry)EndocrinologyInsulin

Abstract

fetched live from OpenAlex

Colorectal cancer liver metastases (CRCLMs) have two main histopathological growth patterns (HPGs): desmoplastic (DHGP) and replacement (RHGP). The vascularization in DHGP tumours is angiogenic, while the RHGP tumours exert vessel co-option vasculature. The presence of vessel co-option tumours is associated with poor response to anti-angiogenic agents and chemotherapy, as well as a worse prognosis. Metformin has been shown to influence the progression and vasculature of tumours in different cancers. However, its role in CRCLM is poorly understood. Herein, we conducted a retrospective cohort study to examine the role of metformin in CRCLM. A dataset of 108 patients was screened, of which 20 patients used metformin. The metformin user patients did not use metformin as an anticancer agent. We noticed a significantly lower percentage of CRCLM patients with vessel co-opting RHGP tumours in the population that used metformin compared to CRCLM patients who did not use metformin. Similar results were obtained when we compared the ratio of recurrence and extrahepatic metastases incidence. Moreover, the metformin user patients had significantly higher survival outcome compared to nonusers. Collectively, our data suggest that metformin administration is likely associated with better prognosis of CRCLM.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

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

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 head0.282
Teacher spread0.267 · 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 designBench or experimental
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

Citations10
Published2023
Admission routes2
Has abstractyes

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