Increased incidence of differentiated thyroid carcinoma and detection of subclinical disease
Bibliographic record
Abstract
BACKGROUND: Recent reports from North America and Europe have documented an annual increase in the incidence of differentiated thyroid carcinoma. We sought to investigate the relation between rates of detection, tumour size, age and sex. METHODS: Using the Ontario Cancer Registry, we identified 7422 cases of differentiated thyroid carcinoma diagnosed from Jan. 1, 1990, to Dec. 31, 2001. We obtained pathology reports for a random 10% of the 7422 patients for each year of the study period. The sample represented all Cancer Care Ontario regions. We compared the size of the patients' tumours by year, sex and age. RESULTS: As expected, the incidence of differentiated thyroid carcinoma increased over the 12-year period. A significantly higher number of small (< or = 2 cm), nonpalpable tumours were resected in 2001 than in 1990 (p = 0.001). The incidence of tumours 2-4 cm in diameter remained stable. When we examined differences in tumour detection rates by age and sex, we observed a disproportionate increase in the number of small tumours detected among women and among patients older than 45 years. INTERPRETATION: Our findings suggest that more frequent use of medical imaging has led to an increased detection rate of small, subclinical tumours, which in turn accounts for the higher incidence of differentiated thyroid carcinoma. This suggests that we need to re-evaluate our understanding of the trends in thyroid cancer incidence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".