Application of Next Generation Quality/Statistical Process Control and Expert-Led Case Review to Increase the Consistency of Diagnostic Rates in Precancerous Colorectal Polyps
Bibliographic record
Abstract
BACKGROUND: Prior work suggests high interrater variability in the pathologist diagnostic rate (PDR) of the precancerous polyp sessile serrated adenoma (SSA). OBJECTIVES: To improve the diagnostic consistency in the pathological evaluation of colorectal polyp specimens with diagnostic rate awareness, using funnel plots (FPs)/control charts (CCs), and a focused group case review. METHODS: All colorectal polyp specimen (CRPS) reports September 2015 to August 2017 were analyzed at one institution. PDRs were extracted using a hierarchical free-text string matching algorithm and visualized using FPs, showing pathologist specimen volume versus PDR, and CCs, showing pathologist versus normed PDR. The FPs/CCs were centered on the group median diagnostic rate (GMDR). Pathologists were shown their baseline SSA diagnostic rate in relation to the practice, and in January 2017, there was a focused group case review/open discussion of approximately 40 sequential cases signed as SSA with a gastrointestinal pathology expert. RESULTS: Nine pathologists interpreted more than 250 CRPSs per year. FPs/CCs for the first and second years showed 6/4 and 3/1 P < .05/P < .001 pathologist outliers, respectively, in relation to the GMDR for SSA and 0/0 and 0/0 P < .05/P < .001 pathologist outliers, respectively, in relation to the GMDR for tubular adenoma (TA). An in silico kappa (ISK) for SSA improved from 0.52 to 0.62. CONCLUSION: Diagnostic rate awareness facilitated by FPs/CCs coupled with focused expert-led reviews may help calibrate PDR. Variation in SSA PDRs still remains high in relation to TA. ISK represents an intuitive, useful metric and Next Generation Quality/Statistical Process Control a promising approach for objectively increasing diagnostic consistency.
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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 | 0.442 | 0.577 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".