Pharmacologic treatment of GERD: Where we are now, and where are we going?
Bibliographic record
Abstract
The introduction of acid inhibition in clinical practice has revolutionized the management of acid-related diseases, leading to the virtual abolition of elective surgery for ulcer disease and relegating antireflux surgery to patients with gastroesophageal reflux disease (GERD) not adequately managed by medical therapy. Proton pump inhibitors (PPIs) are the antisecretory drugs of choice for the treatment of reflux disease. However, these drugs still leave some unmet clinical needs in GERD. PPI-refractoriness is common, and persistent symptoms are observed in up to 40-55% of daily PPI users. Potassium-competitive acid blockers (P-CABs) clearly overcome many of the drawbacks and limitations of PPIs, achieving rapid, potent, and prolonged acid suppression, offering the opportunity to address many of the unmet needs. In recent years, it has been increasingly recognized that impaired mucosal integrity is involved in the pathogenesis of GERD. As a consequence, esophageal mucosal protection has emerged as a new, promising therapeutic avenue. When P-CABS are used as add-on medications to standard treatment, a growing body of evidence suggests a significant additional benefit, especially in the relief of symptoms not responding to PPI therapy. On the contrary, reflux inhibitors are considered a promise unfulfilled, and prokinetic agents should only be used on a case-by-case basis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".