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Record W4391810537 · doi:10.3390/curroncol31020075

Insulin Resistance: The Increased Risk of Cancers

2024· article· en· W4391810537 on OpenAlexvenueno aff
Leszek Szablewski

Bibliographic record

VenueCurrent Oncology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsHyperinsulinemiaInsulin resistanceMedicineInternal medicineCancerAdipose tissueObesityDiabetes mellitusInsulinEndocrinologyType 2 diabetesMetabolic syndromeType 2 Diabetes MellitusMetastasisBioinformaticsOncologyBiology

Abstract

fetched live from OpenAlex

Insulin resistance, also known as impaired insulin sensitivity, is the result of a decreased reaction of insulin signaling to blood glucose levels. This state is observed when muscle cells, adipose tissue, and liver cells, improperly respond to a particular concentration of insulin. Insulin resistance and related increased plasma insulin levels (hyperinsulinemia) may cause metabolic impairments, which are pathological states observed in obesity and type 2 diabetes mellitus. Observations of cancer patients confirm that hyperinsulinemia is a major factor influencing obesity, type 2 diabetes, and cancer. Obesity and diabetes have been reported as risks of the initiation, progression, and metastasis of several cancers. However, both of the aforementioned pathologies may independently and additionally increase the cancer risk. The state of metabolic disorders observed in cancer patients is associated with poor outcomes of cancer treatment. For example, patients suffering from metabolic disorders have higher cancer recurrence rates and their overall survival is reduced. In these associations between insulin resistance and cancer risk, an overview of the various pathogenic mechanisms that play a role in the development of cancer is discussed.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.029
GPT teacher head0.345
Teacher spread0.316 · 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

Citations96
Published2024
Admission routes1
Has abstractyes

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