Accounting for the effect of GERD symptoms on patients’ health-related quality of life: supporting optimal disease management by primary care physicians
Bibliographic record
Abstract
AIM: To review, from a primary care physician (PCP) perspective, the use of patient-reported outcome (PRO) instruments for assessment of gastro-oesophageal reflux disease (GERD) symptoms, their impact on health-related quality of life (HRQL) and the effectiveness of therapy. RESULTS: While generic and disease-specific PRO instruments have been used in the assessment of GERD, the latter can be considered to be more appropriate as they focus only on problems relevant to the disease in question (and therefore tend to be more responsive to change). Such instruments include the Quality of Life in Reflux and Dyspepsia (QOLRAD) questionnaire and the Gastrointestinal Symptom Rating Scale and the Reflux Disease Questionnaire (RDQ). Their use indicates that GERD symptoms are troublesome and significantly reduce patients' HRQL, and that effective treatment of GERD improves HRQL. The GERD Impact Scale (GIS) questionnaire, primarily developed for use within primary care, can also help to determine the impact of symptoms on patients' everyday lives and, in turn, the benefit of appropriately targeted therapy. Notably, these PRO instruments were developed from focus groups of GERD patients, and only aspects rated of highest importance are used in the final instruments. Consequently, PCPs can feel confident that these questionnaires encompass the most relevant points that they are likely to ask in terms of how symptoms affect patients' everyday lives. CONCLUSIONS: Primary care physicians are encouraged to make wider use of PRO instruments within routine practice to improve communication with their GERD patients that, in turn, could lead to improved clinical outcomes and greater patient satisfaction.
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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 | 0.025 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".