Predictive Value of Serum Thyroglobulin After Surgery for Thyroid Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To determine the relationship between stimulated serum thyroglobulin levels (taken 3 months after total thyroidectomy) and tumor stage and recurrence in patients with well-differentiated thyroid carcinoma. STUDY DESIGN: Retrospective chart review in a tertiary care institution. METHODS: Two hundred thirteen consecutive patients with well differentiated thyroid carcinoma treated between 1983 and 1998 were identified. Data were collected on clinicopathological variables, stimulated serum thyroglobulin levels obtained 3 months after total thyroidectomy prior to 131I therapy and recurrence. RESULTS: A high postoperative thyroglobulin level was significantly associated with advanced-stage disease at presentation (P =.005, Kruskall-Wallis) but not with any of the other clinicopathological variables. Patients with a thyroglobulin level greater than 20 pmol/L had a significantly increased risk of disease recurrence on univariate analysis (n = 213 [P =.0001, log rank test]), and in the Cox proportional-hazards model, both advanced tumor stage (P =.001, relative hazard, 3.4 [95% confidence interval [CI]: 2.4-4.9]) and a thyroglobulin level greater than 20 pmol/L (P =.001, relative hazard, 5.1 [95% CI: 2.0-13.1]) were significant predictors of recurrence. No other variables significantly altered the hazards model. CONCLUSIONS: Advanced tumor stage at diagnosis and a stimulated thyroglobulin level greater than 20 pmol/L taken 3 months after total thyroidectomy were independent predictors of disease recurrence. Patients with a thyroglobulin level greater than 20 pmol/L are at increased risk of recurrence and may be candidates for more intensive follow-up or additional treatment.
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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".