Development of a capsule endoscopy scoring index for small bowel mucosal inflammatory change
Bibliographic record
Abstract
BACKGROUND: Capsule endoscopy can identify small bowel mucosal inflammatory change. However, there has been no validated index for capsule endoscopy findings. This manuscript documents the development of such an index. AIM: To develop a capsule endoscopy scoring index for small bowel mucosal inflammatory change. METHODS: The index was created in four separate steps. First, parameters and descriptors of inflammatory change were identified. Secondly, blinded readers prospectively graded the presence or absence of each parameter on de-identified videos and graded a perceived global assessment of overall severity. Thirdly, the individual parameters and descriptors were ranked in order of severity. Fourthly, values for each parameter were created using the descent gradient methodology. The premise was to assure that the final numerical score reflected the global assessment and that the global assessment agreed with the ranking of finding severity. Results were compiled for the three categories: no or clinically insignificant change, mild change, and moderate or severe change. Thresholds were determined. RESULTS: The final index includes three parameters: villous oedema, ulcer and stenosis. A score <135 is designated normal or clinically insignificant mucosal inflammatory change, a score between 135 and 790 is mild, and a score > or = 790 is moderate to severe. CONCLUSION: This capsule endoscopy score provides a common language to quantify small bowel inflammatory changes.
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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 | 0.007 | 0.017 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".