Survival from Differentiated Thyroid Cancer: What Has Age Got to Do with It?
Bibliographic record
Abstract
BACKGROUND: In most staging systems, 45 years of age is used to differentiate low risk thyroid cancer from high risk thyroid cancer. However, recent studies have questioned both the precise 45 year age point and the concept of using a binary cut off as accurate predictors of disease specific mortality. METHODS: A cohort of 3664 thyroid cancer patients that received surgery and adjuvant treatment at Memorial Sloan Kettering Cancer Center (MSKCC) from the years 1985 to 2010 were analyzed to determine the significance of age at diagnosis as a categorical variable at a variety of age cutoffs (5 year intervals between 30 and 70 years of age). The unadjusted and adjusted hazard ratio for the association between disease-specific survival and age was determined using a Cox proportional hazards model adjusted for other predictive variables sex, histology, and pathological T, N, and M status. Furthermore, predictive nomograms of disease-specific mortality were created and validated on an external dataset of 4551 patients to evaluate the impact of age at diagnosis as both a categorical and continuous variable. RESULTS: In the MSKCC cohort, with a median follow-up time of 54 months (range 1-332), there were 59 deaths from thyroid cancer with a 10 year disease-specific survival of 96%. Adjusted hazard ratios for all age cutoffs from age 30 to age 70 years were significant. There was no specific cutoff age which risk stratifies patients with differentiated thyroid cancer (DTC). Categorizing age into five strata (<40, 40-49, 50-59, 60-69 and >70 years) showed a 37-fold increase in hazard ratio from age <40 years to age >70 years. A predictive nomogram using age as a continuous variable with other predictive variables had a high concordance index of 96%. Validation on the external cohort had a concordance index of 73%. CONCLUSIONS: Mortality from DTC increases progressively with advancing age. There is no specific cutoff age which risk stratifies patients with DTC. A predictive nomogram using age as a continuous variable may be a more appropriate tool for stratifying patients with DTC and for predicting outcome.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".