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Record W2808794522 · doi:10.1101/2021.06.04.21258310

The Prevention and Control of Cancer by Metformin in Patients with Type 2 Diabetes: A Systematic Mapping Review

2021· preprint· en· W2808794522 on OpenAlexfundno aff
Albania Mitchell, Michelle Price, Gabriela C. Cipriano

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsnot available
FundersInstitute of Nutrition, Metabolism and DiabetesNational Center for Advancing Translational SciencesNational Center for Research ResourcesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer InstituteFundamental Research Funds for the Central UniversitiesChina-Japan Friendship HospitalPirkanmaan RahastoNorris Cotton Cancer CenterNational Institutes of HealthTaysCenter for Cell Signaling in Gastroenterology, Mayo ClinicRegione PiemonteClinical Science Research and DevelopmentNational Science FoundationChina Medical UniversityNational Science CouncilAsia UniversityChongqing Medical UniversityNational Natural Science Foundation of ChinaNational Research Foundation of KoreaScience and Technology Commission of Shanghai MunicipalityTaipei Medical UniversityCanadian Cancer Society Research InstituteMinistero della SaluteKidney Foundation, JapanCollege ter Beoordeling van GeneesmiddelenAcademia SinicaNational Health Research InstitutesInstitute of Circulatory and Respiratory HealthSusan G. KomenKorea Health Industry Development InstituteSuomen KulttuurirahastoFred C. and Katherine B. Andersen FoundationZonMwAstellas PharmaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungTaiwan BiobankVlaamse regeringNational Research FoundationCancer Care OntarioUniversität BaselNovo NordiskUniversity of PennsylvaniaHeart and Stroke Foundation of CanadaEuropean Foundation for the Study of DiabetesChina Medical University HospitalIrish Cancer SocietySanofiAmerican Academy of DermatologyVanderbilt UniversityShanghai Municipal Education CommissionFederal Emergency Management AgencyGlaxoSmithKlineAssociazione Italiana per la Ricerca sul CancroCongressionally Directed Medical Research ProgramsChinese University of Hong KongU.S. Department of DefenseEli Lilly and CompanyCanadian Institutes of Health ResearchDiabetes UKMayo Foundation for Medical Education and ResearchEuropean CommissionKidney Foundation of CanadaBreast Cancer Research FoundationU.S. Department of Homeland SecurityNational Research Program for BiopharmaceuticalsPfizer
KeywordsMetforminMedicineType 2 diabetesInternal medicineOncologySystematic reviewCancerDiabetes mellitusProstate cancerMeta-analysisMEDLINECochrane LibraryBreast cancerEndocrinologyInsulin

Abstract

fetched live from OpenAlex

ABSTRACT Objective Metformin is commonly used as a first line therapy for type 2 diabetes; however, existing evidence suggests an influence in oncology. The objective of this systematic mapping review was to describe current literature regarding metformin and its role in preventing and /or controlling cancer in patients with type 2 diabetes. Method We searched PubMed, Cochrane Library, and ClinicalTrials.gov in February 2018 and April 2019 to identify research studies, systematic reviews and meta-analyses. Of the 318 citations identified, 156 publications were included in this analysis. Results The most common cancer types researched were colorectal, liver, prostate, lung and breast with the United States contributing the most to this data. Author teams averaged six members and most studies were funded. Only 68% of the articles were available open access. Ovarian and esophageal cancers were amongst the least studied, but the most costly for care.

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)0.008
metaresearch head score (Gemma)0.042
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: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.008
Bibliometrics0.0090.010
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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