The Proportion of Malignancy in Incidental Thyroid Lesions on 18‐FDG PET Study: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate through a systematic review and meta-analysis the malignancy rates of thyroid incidentalomas identified in adults by 18-fluorodeoxyglucose positron emission tomography, computed tomography (18-FDG PET-CT) imaging studies. DATA SOURCES: The literature search was conducted using OVID Medline, EMBASE, the Cochrane Library, Google Scholar, Pubmed, and reference list review (inception to April 2013) by 2 independent review authors. REVIEW METHODS: Studies with adults undergoing 18-FDG PET scan identifying a thyroid incidentaloma with definitive histological or cytological results reported were included. RESULTS: Thirty-one studies with a total of 197,296 PET studies and 3659 focal thyroid incidentalomas were identified with 1341 having definitive cytopathology or histopathology. The pooled proportion of malignancy was calculated as 19.8% (95% confidence interval [CI], 15.3%-24.7%) with 15.4% (95% CI, 11.4%-20.0%) of the total cases being papillary thyroid cancer. Distant metastases represented 1.1% (95% CI, 0.6%-1.8%) of the total cases. CONCLUSIONS: Our systematic review and meta-analysis suggests that the incidence of malignancy is high in thyroid incidentalomas identified through 18-FDG PET imaging studies. Thyroid incidentalomas identified through 18-FDG PET require thorough investigation.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".