Determination of ReQuest<sup>TM</sup>-Based Symptom Thresholds to Define Symptom Relief in GERD Clinical Studies
Bibliographic record
Abstract
Background/Aims: The growing importance of symptom assessment is evident from the numerous clinical studies on gastroesophageal reflux disease (GERD) assessing treatment-induced symptom relief. However, to date, the a priori selection of criteria defining symptom relief has been arbitrary. The present study was designed to prospectively identify GERD symptom thresholds for the broad spectrum of GERD-related symptoms assessed by the validated reflux questionnaire (ReQuestTM) and its subscales, ReQuestTM-GI (gastrointestinal symptoms) and ReQuestTM-WSO (general well-being, sleep disturbances, other complaints), in individuals without evidence of GERD. Methods: In this 4-day evaluation in Germany, 385 individuals without evidence of GERD were included. On the first day, participants completed the ReQuestTM, the Gastrointestinal Symptom Rating Scale, and the Psychological General Well-Being scale. On the other days, participants filled in the ReQuestTM only. GERD symptom thresholds were calculated for ReQuestTM and its subscales, based on the respective 90th percentiles. Results: GERD symptom thresholds were 3.37 for ReQuestTM, 0.95 for ReQuestTM-GI, and 2.46 for ReQuestTM-WSO. Conclusion: Even individuals without evidence of GERD may experience some mild symptoms that are commonly ascribed to GERD. GERD symptom thresholds derived in this study can be used to define the global symptom relief in patients with 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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".