Systematic Review: Do We Need a New Gastro-Oesophageal Reflux Disease Questionnaire?
Bibliographic record
Abstract
BACKGROUND: Gastro-oesophageal reflux disease (GERD) is highly prevalent in Western countries. Because the majority of patients do not present with endoscopic abnormalities, the assessment of the symptom severity and quality of life, and their response to treatment, has become increasingly important. Self-assessed symptom questionnaires are now key instruments in clinical trials. AIM: To evaluate the validity of available GERD measurement tools. METHODS: An ideal GERD symptom assessment instrument, suitable as a primary end-point for clinical trials, should possess the following characteristics: (i) be sensitive in patients with GERD; (ii) cover the frequency and intensity of typical and atypical GERD symptoms; (iii) be multidimensional (cover all symptom dimensions); (iv) have proven psychometric properties (validity, reliability and responsiveness); (v) be practical and economical; (vi) be self-assessed; (vii) use 'word pictures' which are easy to understand for patients; (viii) respond rapidly to changes (responsiveness over short time intervals); (ix) be used daily to assess changes during and after therapy; and (x) be valid in different languages for international use. RESULTS: A literature review revealed five scales that met some of the above characteristics, but did not fulfil all criteria. CONCLUSION: There is a need for a new evaluative tool for the assessment of GERD symptoms and their response to therapy.
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.
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 | 0.027 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".