Development of Reliable, Valid and Responsive Scoring Systems for Endoscopy and Histology in Animal Models for Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Although several endoscopic and histopathologic indices are available for evaluating the severity of inflammation in mouse models of colitis, the reliability of these scoring instruments is unknown. Our aim was to evaluate the reliability of the individual items in the existing indices and develop new scoring systems by selection of the most reliable index items. METHODS: Two observers scored the histological slides [n = 224] and endoscopy videos [n = 201] from treated and untreated Interleukin[IL]-10 knock-out and T-cell transferred SCID mice. Intra-rater and inter-rater reliability for endoscopy and histology scores, and each individual item, were measured using intraclass correlation coefficients [ICCs]. The Mouse Colitis Histology Index [MCHI] and Mouse Colitis Endoscopy Index [MCEI] were developed using the most reliable items. Both were correlated to the colon density and to each other and were evaluated for their ability to detect changes in pathobiology. RESULTS: The intraclass correlation coefficients (ICCs) for inter-rater agreement (95% CIs) for the total histology and endoscopy scores were 0.90 [0.87-0.92] and 0.80 [0.76-0.84], respectively. The MCHI and MCEI were highly correlated with colon density, with a Spearman Rho = 0.81[0.75-0.85] and 0.73 [0.66-0.79], respectively, and with each other, Spearman Rho = 0.71 [0.63-0.77]. The MCHI and MCEI were able to distinguish between the experimental groups within the models, with pairwise differences between the treated and untreated groups being statistically significant [p < 0.001]. CONCLUSIONS: These histological and endoscopic indices are valid and reliable measures of intestinal inflammation in mice, and they are responsive to treatment effects in pre-clinical studies.
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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 | 0.040 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".