Interobserver and Intraobserver Variation Among Experts in the Diagnosis of Thyroid Follicular Lesions With Borderline Nuclear Features of Papillary Carcinoma
Bibliographic record
Abstract
Distinguishing follicular variant of papillary carcinoma (FVPC) from follicular adenoma and follicular carcinoma can be difficult if nuclear features of papillary carcinoma are not well developed or only focally present. We assessed interobserver and intraobserver agreement among 6 thyroid experts by using 15 cases in which original pathologists suspected FVPC. There was unanimous expert agreement in diagnosing FVPC in only 2 cases (13%) and majority agreement in 6 cases (40%). Unanimous agreement on benign and malignant diagnoses was seen in 4 cases (27%) and majority agreement on malignancy in 8 cases (53%). Intraobserver agreement ranged from 17% to 100%. Histologic features considered most helpful in diagnosing FVPC were nuclear clearing, nuclear grooves, nuclear overlapping and crowding, nuclear membrane irregularity, and nuclear enlargement. This considerable interobserver and intraobserver variability in the diagnosis of FVPC seems to result from lack of agreement on the minimal criteria needed to diagnose FVPC, even among experts.
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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 | 0.060 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".