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Record W4401222272 · doi:10.1097/med.0000000000000877

Epidemiologic changes in thyroid disease

2024· review· en· W4401222272 on OpenAlexaff
Gonzalo J. Acosta, Naykky Singh Ospina, Juan P. Brito

Bibliographic record

VenueCurrent Opinion in Endocrinology Diabetes and Obesity · 2024
Typereview
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsInstitute of Nutrition, Metabolism and Diabetes
Fundersnot available
KeywordsMedicineOverdiagnosisThyroid cancerDiseaseLevothyroxineThyroidThyroid diseaseIncidence (geometry)AsymptomaticThyroid nodulesPediatricsIntensive care medicinePathologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To analyze the evolving epidemiologic trends in thyroid disease, focusing on risk factors, underlying drivers of these changes, and their implications on clinical practice and research priorities. RECENT FINDINGS: Thyroid disease remains one of the most prevalent groups of disorders globally, and the shift in its frequency and distribution is multifactorial. The prevalence of hypothyroidism increases with age, although normal thyrotropin ranges appear to be age-dependent, raising concern for potentially inappropriate levothyroxine use. Hyperthyroidism and Graves' disease continue to be predominant in reproductive-age women but exhibit a milder phenotype at diagnosis. Thyroid nodules are increasingly found in asymptomatic patients, likely from more widespread use of neck and chest imaging. Thyroid cancer incidence has risen exponentially over the years, mostly driven by overdiagnosis of low-risk tumors; however, a small rise in incidence of higher risk tumors has been noted. Obesity appears to be a risk factor for thyroid cancer occurrence and more aggressive forms of the disease. SUMMARY: Understanding epidemiologic trends in thyroid disease is crucial for guiding clinical practice and research efforts, aiming to optimize patient outcomes while preventing unnecessary and potentially harmful 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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.094
GPT teacher head0.399
Teacher spread0.306 · 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 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

Citations20
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in Endocrinology Diabetes and ObesitySame topicThyroid Disorders and TreatmentsFrench-language works237,207