Economic Reflections on Proton Pump Inhibitor Therapy for Non-Erosive Reflux Disease
Bibliographic record
Abstract
Proton pump inhibitor (PPI) therapy for gastroesophageal reflux disease is a good example of the evolution of economic analysis. Initial studies were simple models constructed on spreadsheets and described the most cost-effective therapy in terms of cost per cure of esophagitis. This tells a third-party payer what is the most efficient approach to healing esophagitis (technical efficiency) but does not give any indication of whether treating esophagitis is good value for money in the first place or whether health care dollars would be better spent in treating other diseases (allocative efficiency). As economic analyses became more sophisticated, more complex models were constructed. Outcomes were expressed in terms of cost per quality-adjusted life year gained or the question was framed in terms of the probability a strategy would be cost effective depending on willingness to pay for a month free from symptoms. These approaches answer the question of whether treating gastroesophageal reflux disease is good value for money. Models have traditionally evaluated treatment of esophagitis, but this does not address the most efficient therapy of non-erosive reflux disease. This article describes a simplified model (for illustrative purposes only) and suggests that PPI therapy is a cost-effective approach for the treatment of esophagitis whether generic or proprietary PPI costs are applied. PPI therapy is also likely to be a cost-effective strategy for non-erosive reflux disease at generic but not at proprietary prices.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".