Thyroid Cancer in Thyrotoxic Patients: Clinicopathological Features and Outcomes: A Propensity Score Analysis
Bibliographic record
Abstract
Background: Over the past few decades, the incidence of differentiated thyroid cancer (DTC) has continued to rise, largely attributed to advances in diagnostic imaging and increased surveillance. Whether the presence of concomitant thyrotoxicosis alters the behavior or prognosis of DTC remains uncertain and continues to be debated. We conducted a retrospective matched cohort study to address this question. Methods: We retrospectively analyzed 11 patients with thyrotoxicosis and DTC and compared them with 415 euthyroid DTC patients treated between 2010 and 2020. To minimize confounding, a 1:4 propensity score matching analysis was performed. Risk stratification and outcomes were assessed according to the 2015 American Thyroid Association (ATA) guidelines. Results: Papillary thyroid carcinoma was the predominant type in both groups (90.9% in thyrotoxic vs. 89.6% in euthyroid/hypothyroid). Among thyrotoxic patients, six out of 11 (54.5%) had papillary microcarcinomas. After matching, no significant differences were found between the groups in ATA risk category (P = 0.12), or disease outcome at 1 year (P = 0.29). Conclusions: These findings may suggest that thyrotoxicosis facilitates earlier detection of DTC, reflected by the higher proportion of microcarcinomas, but it does not appear to substantially influence tumor behavior or short-term outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".