The impact of illness in patients with moderate to severe gastro-esophageal reflux disease
Bibliographic record
Abstract
BACKGROUND: Gastro-esophageal reflux disease (GERD) is a common disease. It impairs health related quality of life (HRQL). However, the impact on utility scores and work productivity in patients with moderate to severe GERD is not well known. METHODS: We analyzed data from 217 patients with moderate to severe GERD (mean age 50, SD 13.7) across 17 Canadian centers. Patients completed three utility instruments--the standard gamble (SG), the feeling thermometer (FT), and the Health Utilities Index 3 (HUI 3)--and several HRQL instruments, including Quality of Life in Reflux and Dyspepsia (QOLRAD) and the Medical Outcomes Short Form-36 (SF-36). All patients received a proton pump inhibitor, esomeprazole 40 mg daily, for four to six weeks. RESULTS: The mean scores on a scale from 0 (dead) to 1 (full health) obtained for the FT, SG, and HUI 3 were 0.67 (95% CI, 0.64 to 0.70), 0.76 (95% CI, 0.75 to 0.80), and 0.80 (95% CI, 0.77 to 0.82) respectively. The mean scores on the SF-36 were lower than the previously reported Canadian and US general population mean scores and work productivity was impaired. CONCLUSION: GERD has significant impact on utility scores, HRQL, and work productivity in patients with moderate to severe disease. Furthermore, the FT and HUI 3 provide more valid measurements of HRQL in GERD than the SG. After treatment with esomeprazole, patients showed improved HRQL.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".