Development and validation of a histological index for UC
Bibliographic record
Abstract
OBJECTIVE: Although the Geboes score (GS) and modified Riley score (MRS) are commonly used to evaluate histological disease activity in UC, their operating properties are unknown. Accordingly, we developed an alternative instrument. DESIGN: Four pathologists scored 48 UC colon biopsies using the GS, MRS and a visual analogue scale global rating. Intra-rater and inter-rater reliability for each index and individual index items were measured using intraclass correlation coefficients (ICCs). Items with high reliability were used to develop the Robarts histopathology index (RHI). The responsiveness/validity of the RHI and multiple histological, endoscopic and clinical outcome measures were evaluated by analyses of change scores, standardised effect size (SES) and Guyatt's responsiveness statistic (GRS) using data from a clinical trial of an effective therapy. RESULTS: Inter-rater ICCs (95% CIs) for the total GS and MRS scores were 0.79 (0.63 to 0.87) and 0.80 (0.69 to 0.87). The correlation estimates between change scores in RHI and change score in GS and MRS were 0.75 (0.67 to 0.82) and 0.84 (0.79 to 0.88), respectively. The SES and GRS estimates for GS, MRS and RHI were: 1.87 (1.54 to 2.20) and 1.23 (0.97 to 1.50), 1.29 (1.02 to 1.56) and 0.88 (0.65 to 1.12), and 1.05 (0.79 to 1.30) and 0.88 (0.64 to 1.12), respectively. CONCLUSIONS: The RHI is a new histopathological index with favourable operating properties.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".