A241 APPLICATION OF NEXT GENERATION QUALITY AND EXPERT-LED CASE REVIEW TO INCREASE THE CONSISTENCY OF SESSILE SERRATED ADENOMA DIAGNOSTIC RATES: A CANADIAN TERTIARY-CARE CENTRE EXPERIENCE
Bibliographic record
Abstract
Sessile serrated adenomas (SSAs) are known precursors of colorectal adenocarcinoma. Accurate identification and classification of SSAs have important clinical implications. Despite attempts to establish universal criteria for the histologic diagnosis of SSA, significant variability exists in the pathologist diagnostic rate (PDR) of SSAs compared to tubular adenoma (TA). The aim of this study was to evaluate (1) reporting variability in the PDR of SSA in relation to TA, and (2) the effect of expert-led case review on SSA PDR variability using Next Generation Quality (NGQ) and Statistical Process Control (SPC) methodology to identify and address possible diagnostic biases. Colorectal polyp specimen (CRPS) reports obtained from 9 pathologists (interpreting >250 CRPS/year) over 2 years (September 2015 to August 2017) were analysed at a tertiary-care hospital. PDRs were extracted using a hierarchical free text string-matching algorithm and visualized using funnel plots/control charts (FPs/CCs) centered on the group median diagnostic rate (GMDR). Pathologists received feedback regarding their baseline SSA diagnostic rate in relation to the GMDR at the end of year 1. In year 2, a group case review/open discussion of ~40 sequential cases signed as SSA was had with a gastrointestinal pathology expert. PDRs were re-assessed at the end of year 2. An in silico kappa (ISK) was calculated for both years to an accuracy of ~0.01, using the PDRs and a maximal diagnostic overlap assumption. A total of 3656 CRPS/133 SSAs and 3398 CRPS/136 SSAs were analyzed in years 1 and 2, respectively. A total of 400 reports/CRPS were randomly selected and audited, with no categorization error for SSA being identified. The raw SSA PDR mean/median/standard deviation/min-max in year 1 and 2 was 3.9%/3.7%/3.0%/0.0–7.9% and 4.1%/3.8%/2.5%/0.8–8.7%, respectively. FPs/CCs demonstrated 6/4 and 3/2 P<0.05/P<0.001 pathologist outliers for years 1 and 2, respectively, in relation to the GMDR for SSA (figure 1 C, D). FPs/CCs for TA showed 0/0 and 0/0 P<0.05/P<0.001 pathologist outliers in relation to the GMDR in year 1 and 2, respectively (figure 1 A, B). An ISK for SSA improved from 0.52 in year 1 to 0.63 in year 2; the ISK for TA was 0.94 in year 1 and 0.92 in year 2. Cohort variation in the SSA PDR remained high in relation to TA, although diagnostic consistency for SSA appears to have increased in year 2 (number of pathologist outliers decreased, ISK increased). Targeted expert-led review appears to help calibrate the PDR and follow-up data allows reassessment. Thus, ISK may represent an intuitive, useful metric and NGQ a promising approach for objectively increasing diagnostic consistency of PDR. None
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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 | 0.032 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 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".