Is Age Associated with Risk of Malignancy in Thyroid Cancer?
Bibliographic record
Abstract
OBJECTIVES: Many predictive models for risk of malignancy in well-differentiated thyroid cancer (WDTC) have been proposed, and many scoring systems for thyroid cancer prognosis have been established. Age is taken in consideration in all. Our main goal is to establish whether patients' age has a correlation with the rate of malignancy, size, and aggressiveness of the tumor. STUDY DESIGN: Case series with chart review. SETTING: McGill University Thyroid Teaching Hospitals. SUBJECTS AND METHODS: A retrospective analysis of 1022 patients undergoing consecutive thyroidectomy was performed. The patients were divided based on age (<45 and ≥ 45 years). Data were gathered for the size of thyroid nodules, the presence of lymph node (LN) metastasis, and the final thyroid pathology, including the presence of extrathyroidal extension. RESULTS: There were 396 patients younger than 45 years and 626 patients 45 years or older. The rates of malignancy were 67.2% in the first group and 68.7% in the second group (P = .111). When patients were stratified according to different age cutoffs, WDTC and LN metastasis occurred more often in patients younger than 50 years (50.2% vs 43.2%, P = .031 and 18.9% vs 14.1%, P = .0496, respectively). Micropapillary carcinoma occurred more often in patients 50 years or older (23.6% vs 16.1%, P = .0035). CONCLUSIONS: Tumor behavior and rates of WDTC were similar in patients aged <45 and ≥ 45 years. Well-differentiated thyroid cancer occurred more often in patients younger than 50 years, whereas the rate of micropapillary carcinoma occurred more often in patients 50 years or older.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".