Responsiveness of a Histologic Scoring System Compared With Peak Eosinophil Count in Eosinophilic Esophagitis
Bibliographic record
Abstract
INTRODUCTION: The optimal instrument for assessing histologic disease activity in patients with eosinophilic esophagitis (EoE) is unclear. We assessed the responsiveness of the EoE Histologic Scoring System (EoE-HSS) when compared with that of the peak eosinophil count (PEC). METHODS: Histopathology slides were obtained from patients with EoE at baseline and after 8 weeks of treatment with swallowed topical budesonide or elimination diet. Two blinded gastrointestinal pathologists scored biopsies on the EoE-HSS, PEC, and 100-mm visual analog scale (VAS) of overall histologic severity. Change was defined as an improvement by ≥0.5 SD in baseline VAS. Responsiveness was quantified using the standardized effect size (SES) and the probability that the index distinguishes a patient with improvement from a patient without improvement, which is the area under the receiver operating characteristic curve (AUC). Longitudinal validity was assessed using Pearson correlations between changes in EoE-HSS and both PEC and VAS. RESULTS: The EoE-HSS grade (SES 2.18 [95% confidence interval, CI: 1.46-2.88]; AUC 0.73 [95% CI: 0.57-0.84]) and stage (SES 2.07 [95% CI: 1.37-2.77]; AUC 0.73 [95% CI: 0.58-0.84]) were highly responsive, similar to PEC (SES 1.44 [95% CI: 0.80-2.07]; AUC 0.73 [95% CI: 0.58-0.84]). The EoE-HSS grade and stage were more highly correlated with changes in VAS (grade 0.92 [95% CI: 0.86-0.95]; stage 0.89 [95% CI: 0.81-0.94]) than with changes in PEC (grade 0.74 [95% CI: 0.58-0.85]; stage 0.66 [95% CI: 0.47-0.80]). DISCUSSION: The EoE-HSS is highly responsive, performs similarly to PEC, and is better correlated with changes in overall histologic activity in patients with EoE.
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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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".