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Record W3011212537 · doi:10.21037/tcr.2020.03.14

Association between diabetes, obesity, aging, and cancer: review of recent literature

2020· review· en· W3011212537 on OpenAlexaff
Judy Qiang, Lorraine L. Lipscombe, Iliana C. Lega

Bibliographic record

VenueTranslational Cancer Research · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsInstitute for Clinical Evaluative SciencesWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineObesityDiabetes mellitusContext (archaeology)CancerRisk factorGerontologyImmunosenescenceColorectal cancerPopulationEpidemiologyPsychological interventionBreast cancerOncologyInternal medicineBioinformaticsEnvironmental healthEndocrinologyImmunologyBiology

Abstract

fetched live from OpenAlex

Rates of obesity and diabetes have risen significantly in recent years and are projected to increase even further in the coming decades. Obesity and diabetes are associated with increased risk of certain tumours, with the strongest relationships demonstrated for colorectal, post-menopausal breast, and endometrial cancer. Another important risk factor for cancer development is aging. Aging is characterized by chronic inflammation and immunosenescence, and accelerated by obesity, which may further stimulate the development of cancer. In this review, we summarize recent literature on the complex interactions between obesity, diabetes, aging, and cancer risk and mortality. We will also provide an overview of both epidemiological as well as pathophysiologic data and their clinical implications. In the context of an aging population and anticipated rise in rates of obesity and diabetes, a better understanding of how these factors interact and impact on cancer risk and prognosis will be important in helping to guide therapeutic interventions.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.093
GPT teacher head0.434
Teacher spread0.341 · 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 designNot applicable
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

Citations26
Published2020
Admission routes1
Has abstractyes

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