The influence of demographic factors and health-related quality of life on treatment satisfaction in patients with gastroesophageal reflux disease treated with esomeprazole.
Bibliographic record
Abstract
BACKGROUND: The correlation between treatment satisfaction and demographic characteristics, symptoms, or health-related quality of life (HRQL) in patients with gastroesophageal reflux disease (GERD) is unknown. The objective of this study was to assess correlates of treatment satisfaction in patients with GERD receiving a proton pump inhibitor, esomeprazole. METHODS: Adult GERD patients (n = 217) completed demography, symptom, HRQL, and treatment satisfaction questionnaires at baseline and/or after treatment with esomeprazole 40 mg once daily for 4 weeks. We used multiple linear regressions with treatment satisfaction as the dependent variable and demographic characteristics, baseline symptoms, baseline HRQL, and change scores in HRQL as independent variables. RESULTS: Among the demographic variables only Caucasian ethnicity was positively associated with treatment satisfaction. Greater vitality assessed by the Quality of Life in Reflux and Dyspepsia (QOLRAD) and worse heartburn assessed by a four-symptom scale at baseline, were associated with greater treatment satisfaction. The greater the improvement on the QOLRAD vitality (change score), the more likely the patient is to be satisfied with the treatment. CONCLUSIONS: Ethnicity, baseline vitality, baseline heartburn severity, and change in QOLRAD vitality correlate with treatment satisfaction 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".