On-Demand Therapy for Gastroesophageal Reflux Disease
Bibliographic record
Abstract
The following pages summarize the proceedings of a symposium held in May 2006 on the emerging role of on-demand therapy for gastroesophageal reflux disease (GERD). Medical therapy for GERD has undergone significant change in recent years with the advent of effective, but expensive, antisecretory agents. On-demand (patient-driven) therapy is attractive to payers and patients, because it appears to be both cost-effective and convenient. Many individuals appear to accept occasional symptomatic breakthrough in exchange for personal control of their disease. On-demand therapy should be distinguished from intermittent therapy, which is either patient- or physician-driven, but which requires intermittent episodes of continuous therapy followed by discontinuation until symptoms recur. Proton pump inhibitors appear to be effective on-demand agents despite theoretical pharmacodynamic limitations for this class of drug. The available data support the use of on-demand therapy for GERD in uninvestigated reflux disease, nonerosive reflux disease, and possibly mild esophagitis as well. On-demand therapy should not be considered for patients with severe 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 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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".