Reproducibility of histological assessments of disease activity in UC
Bibliographic record
Abstract
OBJECTIVE: Histopathology is potentially an important outcome measure in UC. Multiple histological disease activity (HA) indices, including the Geboes score (GS) and modified Riley score (MRS), have been developed; however, the operating properties of these instruments are not clearly defined. We assessed the reproducibility of existing measures of HA. DESIGN: Five experienced pathologists with GI pathology fellowship training and expertise in IBD evaluated, on three separate occasions at least two weeks apart, 49 UC colon biopsies and scored the GS, MRS and a global rating of histological severity using a 100 mm visual analogue scale (VAS). The reproducibility of each grading system and for individual instrument items was quantified by estimates of intraclass correlation coefficients (ICCs) based on two-way random effects models. Uncertainty of estimates was quantified by 95% two-sided CIs obtained using the non-parametric cluster bootstrap method. Biopsies responsible for the greatest disagreement based on the ICC estimates were identified. A consensus process was used to determine the most common sources of measurement disagreement. Recommendations for minimising disagreement were subsequently generated. RESULTS: Intrarater ICCs (95% CIs) for the total GS, MRS and VAS scores were 0.82 (0.73 to 0.88), 0.71 (0.63 to 0.80) and 0.79 (0.72 to 0.85), respectively. Corresponding inter-rater ICCs were substantially lower: 0.56 (0.39 to 0.67), 0.48 (0.35 to 0.66) and 0.61 (0.47 to 0.72). Correlation between the GS and VAS was 0.62 and between the MRS and VAS was 0.61. CONCLUSIONS: Although 'substantial' to 'almost perfect' ICCs for intrarater agreement were found in the assessment of HA in UC, ICCs for inter-rater agreement were considerably lower. According to the consensus process results, standardisation of item definitions and modification of the existing indices is required to create an optimal UC histological instrument.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".