The development of a magnetic resonance imaging index for fistulising Crohn's disease
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance imaging (MRI) is the gold standard for assessment of perianal fistulising Crohn's disease (CD). The Van Assche index is the most commonly used MRI fistula index. AIMS: To assess the reliability of the Van Assche index, and to modify the instrument to improve reliability and create a novel index for fistulising CD. METHODS: A consensus process developed scoring conventions for existing Van Assche index component items and new items. Four experienced radiologists evaluated 50 MRI images in random order on three occasions. Reliability was assessed by estimates of intraclass correlation coefficients (ICCs). Common sources of disagreement were identified and recommendations made to minimise disagreement. A mixed effects model used a 100 mm visual anologue scale (VAS) for global severity as outcome and component items as predictors to create a modified Van Assche index. RESULTS: Intraclass correlation coefficients (95% confidence intervals) for intra-rater reliability of the original and modified Van Assche indices and the VAS were 0.86 (0.81-0.90), 0.90 (0.86-0.93) and 0.86 (0.82-0.89). Corresponding ICCs for inter-rater reliability were 0.66 (0.52-0.76), 0.67 (0.55-0.75) and 0.58 (0.47-0.66). Sources of disagreement included number, location, and extension of fistula tracts, and rectal wall involvement. A modified Van Assche index (range 0-24) was created that included seven component items. CONCLUSIONS: Although "almost perfect" intra-rater reliability was observed for the assessment of MRI images for fistulising CD using the Van Assche index, inter-rater reliability was considerably lower. Our modification of this index should result in a more optimal instrument.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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 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".