Application of Next Generation Quality/Statistical Process Control and Expert-Led Case Review to Increase the Consistency of Diagnostic Rates in Precancerous Colorectal Polyps
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it