Assessment of Interobserver Agreement Among Gynecologic Pathologists Between Three-Tier Versus Binary Pattern-based Classification Systems for HPV-associated Endocervical Adenocarcinoma
Bibliographic record
Abstract
The three-tier (A vs. B vs. C) pattern-based (Silva) classification system is a strong and fairly reproducible predictor of the risk of lymph node involvement and recurrence of human papillomavirus (HPV)-associated endocervical adenocarcinoma (EA). Recently, a binary pattern-based classification system has been proposed which incorporates the Silva pattern and lymphovascular invasion (LVI) to assign tumors as "low risk" or "high risk" and this may have superior prognostic significance compared with the three-tier system as well as current International Federation of Gynecology and Obstetrics (FIGO) staging of cervix-confined disease. The interobserver reproducibility of this binary system, however, is unknown. Representative slides from 59 HPV-associated EAs (1-3 slides/case) were independently reviewed by 5 gynecologic pathologists who participated in an online training module before the study. In the first review, a pattern was assigned using the three-tier system. On the second review, a "low risk" or "high risk" designation was assigned and the presence or absence of LVI was specifically documented. Interobserver agreement was assessed using Fleiss' kappa. The binary system showed improved interobserver agreement (kappa=0.634) compared with the three-tier system (kappa=0.564), with a higher proportion of cases having agreement between at least 4/5 reviewers (86% vs. 73%). Nineteen and 8 cases showed improved and worse interobserver agreement using the binary system, respectively; the remainder showed no change. 3/5 reviewers showed no intraobserver discrepancy while the remaining 2 did in a small subset of cases (n=2 and 4, respectively). In this study, a binary pattern-based classification system showed improved interobserver agreement compared with the traditional three-tier system.
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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 | 0.066 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| 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".