The follow-up of patients with differentiated thyroid cancer and undetectable thyroglobulin (Tg) and Tg antibodies during ablation
Bibliographic record
Abstract
OBJECTIVE: This retrospective study describes the role of serum thyroglobulin (Tg) in relation to tumor characteristics in the prediction of persistent/recurrent disease in patients with differentiated thyroid cancer (DTC) with negative Tg at the time of ablation. DESIGN: Between 1989 and 2006, 94 out of 346 (27%) patients with DTC had undetectable Tg at the time of 131I ablation and were included in this evaluation. The group of 94 patients consisted of 15 males and 79 females in the age range of 16-89 years with a median follow-up of 8 years (range 1-17). All medical records and follow-up parameters of the 94 patients were evaluated for the occurrence of persistent/recurrent disease. In patients with persistent/recurrent disease hematoxylin-eosin-stained slides of the primary tumors and/or metastatic lesions were also reviewed for histological features including immunostains for Tg. RESULTS: During follow-up, 8 out of 94 (8.5%) patients showed persistent/recurrent disease: in the course of the disease two patients showed Tg positivity, three showed Tg antibody (TgAb) positivity, and the other three showed persistently undetectable Tg and TgAb. Patients who developed Tg and/or TgAb positivity during follow-up had a significantly shorter disease-free survival period when compared with patients with persistently undetectable Tg and TgAb (P<0.006). Histological features were not able to predict the recurrent status. CONCLUSIONS: Follow-up of Tg and TgAb in patients with initially negative Tg and TgAb is useful since a number of patients had shown detectable Tg or TgAb during follow-up indicative for persistent/recurrent disease. Tg and TgAb negativity at the time of ablation is not a predictive determinant for future recurrent status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".