Inter-Observer Variation in the Pathologic Identification of Extranodal Extension in Nodal Metastasis from Papillary Thyroid Carcinoma
Bibliographic record
Abstract
BACKGROUND: Extranodal extension (ENE) in lymph node metastases has been shown to worsen the prognosis of papillary thyroid cancer (PTC). Despite the clinical significance of ENE, there are no stringent criteria for its microscopic diagnosis, and its identification is subject to inter-observer variability. The objective of this study was to determine the level of agreement among expert pathologists in the identification of ENE in PTC cases. METHODS: Eleven expert pathologists from the United States, Italy, and Canada were asked to review 61 scanned slides of representative permanent sections of PTC specimens from Mount Sinai Beth Israel Medical Center in New York. Each slide was evaluated for the presence of ENE. The pathologists were also asked to report the criteria they use to identify ENE. RESULTS: The overall strength of agreement in identifying ENE was only fair (κ = 0.35), and the proportion of observed agreement was 0.68. The proportions of observed agreement for the identification of perinodal structures (fat, nerve, skeletal, and thick-walled vessel involvement) ranged from 0.61 to 0.997. CONCLUSIONS: Overall agreement for the identification of ENE is poor. The lack of agreement results from both variation in pathologists' identification of features and disagreement on the histologic criteria for ENE. This lack of concordance may help explain some of the discordant information regarding prognosis in clinical studies when this feature is identified.
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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 | 0.081 | 0.133 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".