Evaluation of GERD Symptoms during Therapy
Bibliographic record
Abstract
Background/Aims: The changes in gastroesophageal reflux disease (GERD)-related symptoms on treatment are variously described, but currently available questionnaires have shortcomings. We therefore developed a self-assessment reflux questionnaire (ReQuest™). This article describes the process of development and testing. Materials and Methods: For the first version of ReQuest™the symptom spectrum of GERD and the various symptom descriptions were investigated. The 67 identified symptom descriptions were condensed empirically into 6 dimensions, to which a 7th dimension on general well-being was added. The symptom burden of the dimensions was measured by frequency and/or intensity. ReQuest™ was translated into different languages and then tested in focus groups. The initial validation was based on data from a clinical trial of patients with erosive GERD, treated with pantoprazole 20 or 40 mg daily for 28 days. Factor analyses determined the contribution of each symptom to the different dimensions. Additionally, correlation analyses between the identified factors and the dimensions were performed. Results: On the basis of factor analyses, ReQuest™ was reduced to a 60-item scale. The factors generated correlated strongly with the dimensions and confirmed the empirical process mathematically. Conclusion:ReQuest™ provides a valuable, self-assessment tool for evaluating the daily treatment response in patients with erosive 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".