Interobserver variability in assessing dysplasia and architecture in colorectal adenomas: a multicentre Canadian study
Bibliographic record
Abstract
AIMS: Following the introduction of colorectal cancer screening programmes throughout Canada, it became necessary to standardise the diagnosis of colorectal adenomas. Canadian guidelines for standardised reporting of adenomas were developed in 2011. The aims of the present study were (a) to assess interobserver variability in the classification of dysplasia and architecture in adenomas and (b) to determine if interobserver variability could be improved by the adoption of criteria specified in the national guidelines. METHODS: An a priori power analysis was used to determine an adequate number of cases and participants. Twelve pathologists independently classified 40 whole-slide images of adenomas according to architecture and dysplasia grade. Following a wash-out period, participants were provided with the national guidelines and asked to reclassify the study set. RESULTS: At baseline, there was moderate interobserver agreement for architecture (K=0.4700; 95% CI 0.4427 to 0.4972) and dysplasia grade (K=0.5680; 95% CI 0.5299 to 0.6062). Following distribution of the guidelines, there was improved interobserver agreement in assessing architecture (K=0.5403; 95% CI 0.5133 to 0.5674)). For dysplasia grade, overall interobserver agreement remained moderate but decreased significantly (K=0.4833; 95% CI 0.4452 to 0.5215). Half of the cases contained high-grade dysplasia (HGD). Two pathologists diagnosed HGD in ≥75% of cases. CONCLUSIONS: The improvement in interobserver agreement in classifying adenoma architecture suggests that national guidelines can be useful in disseminating knowledge, however, the variability in the diagnosis of HGD, even following guideline review suggests the need for ongoing knowledge-transfer exercises.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".