Variability in Synoptic Reporting of Colorectal Cancer pT4a Category and Lymphovascular Invasion
Bibliographic record
Abstract
CONTEXT.—: Serosal involvement (pT4a category) and lymphovascular invasion have prognostic significance in colorectal carcinoma, but are subject to interobserver variation in assessment. OBJECTIVES.—: To provide the first large-scale assessment of interobserver variability in pT4a category and lymphovascular invasion reporting in real-world practice and to explore the impact of information from guidelines on variability in reporting these features. DESIGN.—: Analysis of 1555 consecutive synoptic reports of colorectal carcinoma was performed using multivariate logistic regression. Interobserver variability before and after the presentation of guideline information was assessed using an image-based survey. RESULTS.—: Significant differences in the odds of reporting pT4a versus pT3 category, detecting lymphovascular invasion of any type, and detecting large vessel invasion were identified among hospital sites and for individual pathologists compared with the median pathologist at the same site. Consistent with these results, interobserver agreement was only moderate in the image-based survey regarding T4a staging and lymphovascular invasion (all κ ≤ 0.57). The provision of information from guidelines did not tend to increase interobserver agreement in the survey, though responses in favor of using an elastic stain increased following recommendations for their use. However, when observers were provided with elastic-stained images, interobserver agreement remained only moderate (κ = 0.55). CONCLUSIONS.—: Real-world reporting of pT4a category and lymphovascular invasion shows substantial variability at both local and regional levels. Our study underscores the need to address these features in quality initiatives, and provides a novel method through which existing synoptic data can be harnessed to monitor reporting patterns and provide individualized feedback.
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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 | 0.030 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".