FDG PET of recurrent or metastatic 131I-negative papillary thyroid carcinoma.
Bibliographic record
Abstract
UNLABELLED: This study reports on the use of FDG PET in the follow-up of papillary thyroid cancer patients with negative findings on 131I total body scans and elevated levels of thyroglobulin after total thyroidectomy. METHODS: Eleven asymptomatic patients with previous papillary thyroid cancer, total thyroidectomy, 131I ablation, and treatment of all known metastases had negative findings on 131I total body scans after therapy but persisting elevations of thyroglobulin when not receiving thyroid hormone. All imaging before PET failed to show persisting tumor. FDG PET was performed on all patients while receiving full thyroid hormone replacement, except for the repeated scan of 1 patient (patient 6). After the PET scan, all patients were referred for supplementary CT, sonography, or biopsy of lesions in the neck. RESULTS: All 11 patients showed FDG uptake in the neck or upper mediastinum-in the initial scan in 10 and in a repeated scan in 1. Sonographically guided biopsy confirmed malignancy in 6, was nondiagnostic in 2, and showed normal findings in 1. In 2 patients, the sonographic results were normal and no biopsy was attempted. FDG imaging redirected the treatment of 7 patients, resulting in surgery and external beam radiotherapy in 3, surgery in 1, and external beam radiotherapy in 2. One patient declined further recommended surgery. The other 4 patients remain under observation. Surgical histopathology confirmed thyroid tumor in all 4 surgically treated patients. Retrospective review of the original histopathology slides showed no preponderance of aggressive histology. CONCLUSION: FDG PET is able to guide further evaluation of thyroid cancer patients who have elevated thyroglobulin levels and normal findings on 131I whole-body scanning.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".