Data-driven Thyroglobulin Cutoffs for Low- and Intermediate-risk Thyroid Cancer Follow-up: ITCO Real-world Analysis
Bibliographic record
Abstract
CONTEXT: The utility of thyroglobulin (Tg) in the follow-up of patients with differentiated thyroid cancer has been well-documented. Although third-generation immunoassays have improved accuracy, limitations persist (interfering anti-Tg antibodies and measurement variability). Evolving treatment strategies require a reevaluation of Tg thresholds for optimal patient management. OBJECTIVE: To assess the performance of serum Tg testing in 2 populations: patients receiving total thyroidectomy and radioiodine remnant ablation (RRA) or treated with thyroidectomy alone. DESIGN: Prospective observational study. SETTING: Centers contributing to the Italian Thyroid Cancer Observatory database. PATIENTS: We included 540 patients with 5 years of follow-up and negative anti-Tg antibodies. INTERVENTIONS: Serum Tg levels assessed at 1-year follow-up visit. MAIN OUTCOME MEASURE: Detection of structural disease within 5 years of follow-up. RESULTS: After excluding 26 patients with structural disease detected at any time point, the median Tg did not differ between patients treated with or without radioiodine. Data-driven Tg thresholds were established based on the 97th percentile of Tg levels in disease-free individuals: 1.97 ng/mL for patients undergoing thyroidectomy alone (lower than proposed by the Memorial Sloan Kettering Cancer Center protocol and ESMO Guidelines, yet demonstrating good predictive ability, with a negative predictive value of 98% and 0.84 ng/mL for patients receiving postsurgical RRA. High sensitivity and negative predictive value supported the potential of these thresholds in excluding structural disease. CONCLUSION: This real-world study provides evidence for the continued reliability of 1-year serum Tg levels. The data-driven Tg thresholds proposed offer valuable insights for clinical decision-making in patients undergoing total thyroidectomy with or without RRA.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".