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Record W4415179199 · doi:10.58931/cdet.2025.3243

Thyroid Nodules: Reducing Overdiagnosis and Investigations

2025· article· en· W4415179199 on OpenAlexaff
Vicki Munro, Syed Ali Imran

Bibliographic record

VenueCanadian Diabetes & Endocrinology Today · 2025
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOverdiagnosisThyroidPopulationIncidence (geometry)Thyroid nodulesComputed tomographyNodule (geology)

Abstract

fetched live from OpenAlex

Thyroid nodules (TN) are incredibly common, with approximately 5% of the population presenting with palpable TN. However, the widespread utilization of sensitive imaging techniques over the past few decades has led to a rapid increase in their prevalence. Notwithstanding the clinically palpable TN, the rate of incidental nodules picked up on imaging studies varies remarkably with the underlying imaging modality. For instance, the prevalence of TNs on computed tomography (CT) scans of the neck is reported to be 16.5%,3 FDG-PET to be 2% and on neck ultrasounds (US) well over 50%. The prevalence of TN is higher in females and increases with age, and while there has been little change in the overall reported incidence of palpable TNs, the rising prevalence can be almost exclusively attributed to the expanded use of imaging, particularly the widespread availability of sensitive US. It is estimated that over 60% of the population may have at least one TN.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.991

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 head0.009
GPT teacher head0.248
Teacher spread0.238 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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