The Construction of a New Evaluative GERD Questionnaire – Methods and State of the Art
Bibliographic record
Abstract
Gastroesophageal reflux disease (GERD) is one of the most prevalent diseases worldwide, and it is becoming increasingly important to monitor the effect of various interventions on GERD symptoms. There can be rapid temporal changes in the severity and frequency of patients' symptoms as well as their health status and well-being, all of which could, theoretically, be monitored using diaries or questionnaires. However, current GERD monitoring instruments are not appropriate because they do not assess symptoms daily, they are not sufficiently responsive to short-term changes in health status or they are not adequately validated. To address these problems, the conceptual and psychometric requirements for a GERD symptom assessment questionnaire were identified. A dimension-based scale was designed to reduce the number of symptoms monitored on a daily basis, and the validation process was defined to produce parallel long and short forms of a scale for patients' self-assessment of their GERD symptom response to therapy. These basic principles which underlie the successful development of a new, self-assessed symptomatic reflux questionnaire (ReQuest) are also applicable to the development of validated questionnaires for daily symptom self-assessment in other disease areas.
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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 | 0.034 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".