Recurrence After Treatment of Micropapillary Thyroid Cancer
Bibliographic record
Abstract
BACKGROUND: Despite very low mortality associated with micropapillary thyroid cancer, locoregional recurrence is common and controversy exists regarding optimal surgical treatment and the role of adjunctive radioiodine. METHODS: The National Thyroid Cancer Treatment Cooperative Study Group Registry was analyzed for recurrences in patients with unifocal versus multifocal micropapillary cancer, with or without nodal disease, depending upon the extent of surgery and the use of adjunctive radioiodine. Six hundred eleven patients considered disease-free after initial therapy were followed for 2572 person-years. RESULTS: Thirty patients (6.2%) had recurrences detected at a mean 2.8 years after primary treatment. Recurrences did not differ between patients with unifocal and multifocal disease overall; however, among patients who received less than a near-total thyroidectomy (NTT), those with multifocal disease had more recurrences than those with unifocal disease (18% vs. 4%, p = 0.01). Patients with multifocal disease who had a total (T) or NTT trended toward fewer recurrences than those undergoing less than an NTT (6% vs. 18%, p = 0.058). In patients who did not receive radioiodine therapy, recurrence was more common in patients with multifocal disease versus unifocal disease (7% vs. 2%, p = 0.02). However, radioiodine did not reduce recurrences in patients with multifocal disease or patients with positive nodes. Patients with positive nodes had more recurrences than node-negative patients regardless of surgical extent or use of radioiodine. CONCLUSIONS: Patients with micropapillary multifocal disease have a reduced risk of recurrence after a T/NTT compared with less surgery. A randomized, controlled trial is necessary and feasible to determine if radioiodine ablation of thyroid remnants is advantageous in patients with intrathyroidal micropapillary cancer.
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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 | 0.000 | 0.003 |
| 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) | 0.001 | 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".