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Record W4406690108 · doi:10.1089/ct.2025;37.33-36

Increased Incidence or Detection Bias? Global Cohort Study Finds No Link between GLP1-RAs and Thyroid Cancer Risk

2025· article· en· W4406690108 on OpenAlexaboutno aff
Omar El Kawkgi

Bibliographic record

VenueClinical Thyroidology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIncidence (geometry)Thyroid cancerOncologyCohortCohort studyInternal medicineThyroidEnvironmental health

Abstract

fetched live from OpenAlex

BackgroundGlucagon-like peptide-1 receptor agonists (GLP1-RAs) are a class of glucose-lowering drugs commonly used for the management of type 2 diabetes and obesity given their favorable cardiovascular and weight management outcomes. 1The use of GLP1-RAs has rapidly increased in recent years. 2 However, in the midst of their increasing use, concerns have arisen regarding the risk of thyroid cancer that may be associated with these agents.Prior research has shown conflicting evidence regarding whether GLP1-RAs increase thyroid cancer risk, [3][4][5][6] with biologic plausibility grounded in the expression of GLP1 receptors in papillary thyroid cancer cells 7 and prior animal studies demonstrating an excess in C-cell malignancies in rodents. 8 Given the growing use of GLP1-RAs in managing diabetes and obesity, this study aims to clarify the potential association between these drugs and thyroid cancer risk through a large multisite cohort study using data from six international databases. 9Methods This cohort study analyzed data from 2007 to 2023 from population-based databases in Canada,

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.037
GPT teacher head0.375
Teacher spread0.338 · 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.

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

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