The changing face of thyroid cancer in a population‐based cohort
Bibliographic record
Abstract
In North America, the incidence of thyroid cancer is increasing by over 6% per year. We studied the trends and factors influencing thyroid cancer incidence, its clinical presentation, and treatment outcome during 1970-2010 in a population-based cohort of 2306 consecutive thyroid cancers in Canada, that was followed up for a median period of 10.5 years. Disease-specific survival (DSS) and disease-free survival were estimated by the Kaplan-Meier method and the independent influence of various prognostic factors was evaluated by Cox proportional hazard models. Cumulative incidence of deaths resulting from thyroid cancer was calculated by competing risk analysis. A P-value <0.05 was considered to indicate statistical significance. The age standardized incidence of thyroid cancer by direct method increased from 2.52/100,000 (1970) to 9.37/100,000 (2010). Age at diagnosis, gender distribution, tumor size, and initial tumor stage did not change significantly during this period. The proportion of papillary thyroid cancers increased significantly (P < 0.001) from 58% (1970-1980) to 85.9% (2000-2010) while that of anaplastic cancer fell from 5.7% to 2.1% (P < 0.001). Ten-year DSS improved from 85.4% to 95.6%, and was adversely influenced by anaplastic histology (hazard ratio [HR] = 8.7; P < 0.001), male gender (HR = 1.8; P = 0.001), TNM stage IV (HR = 8.4; P = 0.001), incomplete surgical resection (HR = 2.4; P = 0.002), and age at diagnosis (HR = 1.05 per year; P < 0.001). There was a 373% increase in the incidence of thyroid cancer in Manitoba with a marked improvement in the thyroid cancer-specific survival that was independent of changes in patient demographics, tumor stage, or treatment practices, and is largely attributed to the declining proportion of anaplastic thyroid cancers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".