Histopathological evaluation of duodenal biopsy in the PreventCD project. An observational interobserver agreement study
Bibliographic record
Abstract
Aim of the current study was to evaluate the inter-observer agreement between pathologists in the diagnosis of celiac disease (CD), in the qualified context of a multicenter study. Biopsies from the "PreventCD" study, a multinational- prospective- randomized study in children with at least one-first-degree relative with CD and positive for HLA-DQ2/HLA-DQ8. Ninety-eight biopsies were evaluated. Considering diagnostic samples with villous atrophy (VA), the agreement was satisfactory (κ = 0.84), but much less when assessing the severity of these lesions. The use of the recently proposed Corazza-Villanacci classification showed a moderately higher level of agreement (κ = 0.39) than using the Marsh-Oberhuber system (κ = 0.31). 57.1% of cases were considered correctly oriented. A number of >4 samples per patient was statistically associated to a better agreement; orientation did not impact on κ values. Agreement results in this study appear more satisfactory than in previous papers and this is justified by the involvement of centers with experience in CD diagnosis and by the well-controlled setting. Despite this, the reproducibility was far from optimal with a poor agreement in grading the severity of VA. Our results stress the need of a minimum of four samples to be assessed by the pathologist.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".