Knowledge gaps in the management of refractory reflux‐like symptoms: Healthcare provider survey
Bibliographic record
Abstract
BACKGROUND: Refractory reflux-like symptoms have a substantial impact on patients and healthcare providers. The aim of the survey was to qualitatively assess the needs and attitudes of practicing clinicians around the management of refractory reflux symptoms and refractory gastroesophageal reflux disease (rGERD). METHODS: An International Working Group for the Classification of Oesophagitis (IWGCO) steering committee invited clinicians to complete an online survey including 17 questions. KEY RESULTS: Of the 113 clinicians who completed the survey, 70% were GIs, 20% were primary care physicians, and 10% were other specialties. Functional heartburn was considered the most common reason for an incomplete response to proton pump inhibitor (PPI) therapy (82%), followed by stress/anxiety (69%). More GIs identified esophageal hypersensitivity as a cause, while more non-GIs identified esophageal dysmotility and non-reflux-related esophageal conditions. As the first step, most clinicians would order investigations (70-88%). Overall, 72% would add supplemental therapy for patients with partial response, but only 58% for those with non-response. Antacid/alginate was the most common choice overall, while non-GIs were more likely to add a prokinetic than were GIs (47.8 vs. 24.1%). Approximately 40% of clinicians would switch PPIs in patients with partial response, but only 29% would do so in non-responders. Preferences for long-term therapy were highly variable. The most common initial investigation was upper endoscopy. Choice of esophageal manometry and pH monitoring was more variable, with no clear preference for whether pH monitoring should be conducted on, or off, PPI therapy. CONCLUSIONS AND INFERENCES: The survey identified a number of challenges for clinicians, especially non-GI physicians, treating patients with refractory reflux-like symptoms or rGERD on a daily 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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".