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Record W3096037482 · doi:10.9778/cmajo.20200095

Incidental detection, imaging modalities and temporal trends of differentiated thyroid cancer in Ontario: a population-based retrospective cohort study

2020· article· en· W3096037482 on OpenAlexafffundvenueabout
Todd A. Norwood, Emmalin Buajitti, Lorraine L. Lipscombe, Thérèse A. Stukel, Laura C. Rosella

Bibliographic record

VenueCMAJ Open · 2020
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsCancer Care OntarioWomen's College HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineThyroid cancerRetrospective cohort studyIncidence (geometry)ThyroidCancerPopulationCohortInternal medicinePathology

Abstract

fetched live from OpenAlex

Background: Incidence rates of thyroid cancer in Ontario have increased more rapidly than those of any other cancer, whereas mortality rates have remained relatively stable. We evaluated the extent to which incidental detection of differentiated thyroid cancer during unrelated prediagnostic imaging procedures contributed to Ontario’s incidence rates. Methods: We conducted a retrospective cohort study involving Ontarians who received a diagnosis of differentiated thyroid cancer from 1998 to 2017 using linked health care administrative databases. We classified cases as incidentally detected if a nonthyroid diagnostic imaging test (e.g., computed tomography [CT]) preceded an index event (e.g., prediagnostic fine-needle aspiration biopsy); all other cases were nonincidentally detected cases. We used Joinpoint and negative binomial regressions to characterize sex-specific rates of differentiated thyroid cancer by incidentally detected status and to quantify potential age, diagnosis period and birth cohort effects. Results: The study included 36 531 patients with differentiated thyroid cancer, of which 78.7% were female. Incidentally detected cases increased from 7.0% to 11.0% of female patients and from 13.5% to 18.2% of male patients over the study period. Age-standardized incidence rates increased more rapidly for incidentally detected cases (4.2-fold for female and 3.7-fold for male patients) than for nonincidentally detected cases (2.6-fold for female and 3.0-fold for male patients; p < 0.001). Diagnosis period was the primary factor associated with increased incidence rates of differentiated thyroid cancer, adjusting for other factors. Within each period, incidentally detected rates increased faster than nonincidentally detected rates, adjusting for age. Our results showed that CT was the most common imaging procedure preceding incidentally detected diagnoses. Interpretation: Incidentally detected cases represent a large and increasing component of the observed increases in differentiated thyroid cancer in Ontario over the past 20 years, and CT scans are primarily associated with these cases despite the modality having similar, increasing rates of use compared with magnetic resonance imaging (1993–2004). Recent increases in rates of differentiated thyroid cancer among males and incidentally detected cases among females in Ontario appear to be unrelated to birth cohort effects.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.120
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.293
Teacher spread0.273 · 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 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

Citations5
Published2020
Admission routes4
Has abstractyes

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