New algorithm for the treatment of gastro‐oesophageal reflux disease
Bibliographic record
Abstract
BACKGROUND: Gastro-oesophageal reflux disease (GERD) is associated with a variety of typical and atypical symptoms. Patients often present in the first instance to a pharmacist or primary care physician and are subsequently referred to secondary care if initial management fails. Guidelines usually do not provide a clear guidance for all healthcare professionals with whom the patient may consult. AIM: To update a 2002-treatment algorithm for GERD, making it more applicable to pharmacists as well as doctors. METHODS: A panel of international experts met to discuss the principles and practice of treating GERD. RESULTS: The updated algorithm for the management of GERD can be followed by pharmacists, for over-the-counter medications, primary care physicians, or secondary care gastroenterologists. The algorithm emphasizes the importance of life style changes to help control the triggers for heartburn and adjuvant therapies for rapid and adequate symptom relief. Proton pump inhibitors will remain a prominent treatment for GERD; however, the use of antacids and alginate-antacids (either alone or in combination with acid suppressants) is likely to increase. CONCLUSION: The newly developed algorithm takes into account latest clinical practice experience, offering healthcare professionals clear and effective treatment options for the management of GERD.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".