Mathematical model of the relationship between pH holding time and erosive esophagitis healing rates
Bibliographic record
Abstract
Abstract Effective suppression of gastric acid secretion promotes healing of erosive esophagitis. Treatment guidelines recommend proton pump inhibitors (PPIs) and histamine H2–receptor antagonists (H2RAs). Emerging evidence also supports potassium‐competitive acid blockers (P‐CABs). The aim was to construct a mathematical model to examine the relationship between pH holding time ratios (HTRs) and erosive esophagitis healing rates with H2RAs, PPIs and P‐CABs. By literature search, we identified studies of H2RAs, PPIs or P‐CABs that reported mean pH >4 HTRs at steady state (days 5–8) and erosive esophagitis healing rates after 4 and/or 8 weeks. We aggregated treatments by drug class and developed a non‐linear, mixed‐effects model to explore the relationship between pH >4 HTRs and healing rates. The pH dataset included 82 studies (4297 participants; 201 dosage arms); healing rate data came from 103 studies (43,417 patients; 196 treatment arms). P‐CABs achieved the longest periods with intragastric pH >4, and the highest healing rates after 4 and 8 weeks. The predicted probabilities of achieving ≥90% healing rates at 8 weeks were 74.1% for P‐CABs, 17.3% for PPIs and 0% for H2RAs. P‐CABs provide the longest duration with intragastric pH >4 and, accordingly, the highest healing rates of erosive esophagitis.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".