18F-fluorodeoxyglucose positron emission tomography-computed tomography for suspected recurrent papillary thyroid cancer: early experience at Sunnybrook Health Sciences Centre.
Bibliographic record
Abstract
OBJECTIVES: To report the initial experience with combined 18F-fluorodeoxyglucose positron emission tomography (FDG PET)/computed tomography (CT) imaging for suspected recurrent papillary differentiated thyroid cancer (DTC) at Sunnybrook Health Sciences Centre (SHSC), Toronto. DESIGN: Single institution retrospective study. METHODS: Consecutive patients from SHSC who underwent FDG PET/CT imaging for suspected recurrent DTC over a period of 2.5 years were identified and their charts reviewed. MAIN OUTCOME MEASURE: Qualitative appraisal of FDG PET/CT imaging in suspected recurrent DTC. RESULTS: Sixteen patients (14F, 2M) were identified accounting for 17 FDG PET/CT scans. Three scans (18%) in 3 different patients were reported as suspicious for recurrent disease in the neck (1-3 lesions) and were considered "positive". All were subsequently confirmed pathologically (4-13 positive lymph nodes post operatively). Prior conventional imaging was abnormal in two patients. Two patients had an elevated non-stimulated thyroglobulin (TG) < 10 ng/mL (4.9 and 9.4). The remaining patient had a TG < 0.3 ng/mL but was anti-TG antibody positive (84 IUx10-3/L). With a median follow up of 15 months (range 7-36) there were no false positive or negative scans. Incidental pathology (breast cancer, large bowel polyps) was identified on a further 2/17 scans (12%). CONCLUSIONS: FDG PET/CT imaging is able to detect recurrent DTC in patients with low TG levels. It can complement conventional imaging findings and exclude distant FDG-avid metastases prior to surgery. It may underestimate the number of positive lymph nodes in the neck. Occult pathology may be identified with whole body FDG PET/CT.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".