Observer Variation in the Diagnosis of Follicular Variant of Papillary Thyroid Carcinoma
Bibliographic record
Abstract
The histopathologic diagnosis of follicular variant of papillary thyroid carcinoma (FVPCA) can be difficult. Recent reports have suggested that this neoplasm may be frequently overdiagnosed by pathologists. We examined the observer variation in the diagnosis of FVPCA in 87 tumors by 10 experienced thyroid pathologists. The criteria that the reviewers considered most helpful for making a diagnosis of FVPCA were also assessed. A concordant diagnosis of FVPCA was made by all 10 reviewers with a cumulative frequency of 39%. In this series, 24.1% of the patients had metastatic disease (n = 21). In the cases with metastatic disease, a diagnosis of FVPCA was made by all 10 reviewers with a cumulative frequency of 66.7%, and 7 of the reviewers made a diagnosis of FVPCA with a cumulative frequency of 100%. The most important criteria used to diagnose FVPCA included the presence of cytoplasmic invaginations into the nucleus (pseudo-inclusions), abundant nuclear grooves, and ground glass nuclei. These results suggest that although the diagnosis of FVPCA is variable even among experienced thyroid pathologists, most reviewers agreed on this diagnosis for patients with metastatic disease. The use of well-defined histopathologic features should improve the consistency in diagnosing FVPCA. Since most cases with metastatic disease had obvious invasion, caution should be used in making a diagnosis of FVPCA in the absence of the major histopathologic features or clear-cut invasive growth.
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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 | 0.033 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".