New algorithm for the treatment of gastro‐oesophageal reflux disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it