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Record W4286224310 · doi:10.1097/med.0000000000000756

Women and thyroid cancer incidence: overdiagnosis versus biological risk

2022· article· en· W4286224310 on OpenAlexaff
Diana K Lam, Louise Davies, Anna M. Sawka

Bibliographic record

VenueCurrent Opinion in Endocrinology Diabetes and Obesity · 2022
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsToronto General HospitalUniversity Health NetworkWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsOverdiagnosisThyroid cancerMedicineContext (archaeology)CancerIncidence (geometry)EpidemiologyDiseaseThyroidInternal medicineOncologyDemographyGynecologyBiology

Abstract

fetched live from OpenAlex

PURPOSE OF THE REVIEW: Our aim is to discuss the concepts of sex and gender in the context of thyroid cancer epidemiology. RECENT FINDINGS: It has been long-established in global epidemiologic data that thyroid cancer incidence rates are higher in women than men. However, what has been less well understood is whether this reflects sex disparities in cancer susceptibility, gender disparities in detection, or a combination. A recent meta-analysis of autopsy data from individuals who were not known to have thyroid cancer in their lifetime demonstrated no difference in the prevalence of thyroid cancer in women and men, suggesting that gender differences may be the reason for gender-based differences in thyroid cancer detection. This finding, and sex differences in auto immunity and other factors that may affect cancer susceptibility are explored. SUMMARY: Additional research to explore gender- and sex-specific data on thyroid cancer would inform our understanding of the differences and similarities between men and women in susceptibility and detection of thyroid cancer and help to optimize disease management for all genders and both sexes.

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 score0.032
Threshold uncertainty score0.666

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 head0.043
GPT teacher head0.325
Teacher spread0.282 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

Explore more

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