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Record W4402647866 · doi:10.1002/psp4.13235

Mathematical model of the relationship between pH holding time and erosive esophagitis healing rates

2024· article· en· W4402647866 on OpenAlexaff
Colin W. Howden, Carmelo Scarpignato, Eckhard Leifke, Darcy J. Mulford, Gëzim Lahu, Axel Facius, Yuhong Yuan, Richard H. Hunt

Bibliographic record

VenueCPT Pharmacometrics & Systems Pharmacology · 2024
Typearticle
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsEsophagitisGastric acidMedicineGastroenterologyReflux esophagitisInternal medicineStomachRefluxDisease

Abstract

fetched live from OpenAlex

Abstract Effective suppression of gastric acid secretion promotes healing of erosive esophagitis. Treatment guidelines recommend proton pump inhibitors (PPIs) and histamine H 2 –receptor antagonists (H 2 RAs). 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 H 2 RAs, PPIs and P‐CABs. By literature search, we identified studies of H 2 RAs, 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 H 2 RAs. P‐CABs provide the longest duration with intragastric pH >4 and, accordingly, the highest healing rates of erosive esophagitis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.382
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2024
Admission routes1
Has abstractyes

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