Is the reflux disease questionnaire useful for identifying GERD according to the Montreal definition?
Bibliographic record
Abstract
BACKGROUND: Scales for aiding physicians diagnose gastro-oesophageal reflux disease (GERD) have not been evaluated in terms of their ability to discriminate between troublesome symptoms (TS) and non-troublesome symptoms (NTS). Our objective is to evaluate the ability of the Reflux Disease Questionnaire (RDQ) to identify GERD according to referral of TS, in patients without previous proton pump inhibitor (PPI) treatment and in patients on PPI treatment. METHODS: Patients consulting physicians because of heartburn or acid regurgitation were recruited at 926 primary-care centres in Spain. They were asked to complete several questionnaires including the RDQ, and to define which of their symptoms were troublesome. Information on drug treatment was collected by the physician. We performed a receiver operating characteristic (ROC) curve analysis to ascertain the RDQ's optimum cut-point for identifying TS. RESULTS: 4574 patients were included, 1887 without PPI and 2596 on PPI treatment. Among those without PPI treatment, 1722 reported TS. The area under the curve (AUC) was 0.79 for the RDQ, and the optimum RDQ cut-point for identifying TS was 3.18 (sensitivity, 63.2%; specificity, 80.2%). A total of 2367 patients on PPI treatment reported TS, and the optimum RDQ cut-off value was 3.06 (sensitivity, 65.4%; specificity, 71.8%). CONCLUSIONS: An RDQ score higher than 3 shows good sensitivity and specificity for differentiating TS from NTS among patients without PPI or on PPI treatment. The RDQ is useful in primary care for diagnosis of GERD based on the Montreal definition.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".