An International Multi-Institutional Validation of Age 55 Years as a Cutoff for Risk Stratification in the AJCC/UICC Staging System for Well-Differentiated Thyroid Cancer
Bibliographic record
Abstract
BACKGROUND: Age is a critical factor in outcome for patients with well-differentiated thyroid cancer. Currently, age 45 years is used as a cutoff in staging, although there is increasing evidence to suggest this may be too low. The aim of this study was to assess the potential for changing the cut point for the American Joint Committee on Cancer/Union for International Cancer Control (AJCC/UICC) staging system from 45 years to 55 years based on a combined international patient cohort supplied by individual institutions. METHODS: A total of 9484 patients were included from 10 institutions. Tumor (T), nodes (N), and metastasis (M) data and age were provided for each patient. The group was stratified by AJCC/UICC stage using age 45 years and age 55 years as cutoffs. The Kaplan-Meier method was used to calculate outcomes for disease-specific survival (DSS). Concordance probability estimates (CPE) were calculated to compare the degree of concordance for each model. RESULTS: Using age 45 years as a cutoff, 10-year DSS rates for stage I-IV were 99.7%, 97.3%, 96.6%, and 76.3%, respectively. Using age 55 years as a cutoff, 10-year DSS rates for stage I-IV were 99.5%, 94.7%, 94.1%, and 67.6%, respectively. The change resulted in 12% of patients being downstaged, and the downstaged group had a 10-year DSS of 97.6%. The change resulted in an increase in CPE from 0.90 to 0.92. CONCLUSIONS: A change in the cutoff age in the current AJCC/UICC staging system from 45 years to 55 years would lead to a downstaging of 12% of patients, and would improve the statistical validity of the model. Such a change would be clinically relevant for thousands of patients worldwide by preventing overstaging of patients with low-risk disease while providing a more realistic estimate of prognosis for those who remain high risk.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".