Evaluation of Indeterminate Thyroid Cytology by Second-Opinion Diagnosis or Repeat Fine-Needle Aspiration: Which Is the Best Approach?
Bibliographic record
Abstract
OBJECTIVE: This study investigated a published series evaluating the role of second-opinion diagnosis (SOD) or repeat fine-needle aspiration cytology (RFNA) for indeterminate thyroid aspirates. STUDY DESIGN: Twenty-three studies were selected and the following parameters were analyzed: disagreement between SOD or RFNA and the original diagnosis (OD), reclassification of OD according to the Bethesda system for reporting thyroid cytopathology, the rate of definitive diagnosis and the diagnostic performance of SOD and RFNA. RESULTS: 7,154 thyroid FNAs were retrieved from 9 studies that investigated the role of SOD, including 1,048 (14.6%) cases originally reported as indeterminate. The 14 studies that analyzed the role of thyroid RFNA comprised 67,581 FNAs and included 7,246 (10.7%) indeterminate cases. A definitive diagnosis was achieved by SOD in 450 cases (42.9%) and RFNA in 1,645 cases (57.2%, p=0.0001). Based on cases with histological follow-up, SOD demonstrated significantly higher rates of positive predictive value and accuracy than RFNA (55.8 vs. 37.7%, p=0.0001; 67.4 vs. 56.0%, p=0.0034, respectively). CONCLUSIONS: Both SOD and RFNA demonstrated an improvement in the diagnosis of initially indeterminate thyroid FNAs. RFNA achieved a definitive diagnosis for the majority of indeterminate cases. Regarding histological follow-up, SOD was shown to be more accurate than RFNA.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".