Letter: gastritis in paediatric patients with coeliac disease – authors' reply
Bibliographic record
Abstract
We appreciate the comments by Banaszkiewicz et al., describing gastritis in 197 children with coeliac disease who underwent concurrent duodenal and gastric biopsies.1, 2 In addition to their focus on children (as compared to our study which describes gastritis in subjects of all ages) and the availability of coeliac disease serologies, this report builds upon our study by including duration of symptoms, a variable that was not available in our analysis. Additional clinical data that would be worth evaluating in future analyses of the ‘coeliac stomach’ include the presence of iron deficiency (which may be exacerbated by gastritis) and the use of acid suppression medications, which affect the gastric milieu and may modulate the risk of coeliac disease.3 We would also like to know how these gastritis subtypes change after institution of the gluten-free diet, or during a gluten challenge in an individual whose coeliac disease status is uncertain. In this era of emerging nondietary therapies for coeliac disease,4 the stomach may be another biomarker for gluten exposure in a subset of individuals. The authors' declarations of personal and financial interests are unchanged from those in the original article.2
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".