Do physicians correctly assess patient symptom severity in gastro‐oesophageal reflux disease?
Bibliographic record
Abstract
BACKGROUND: The accuracy of physicians' assessment of the severity of gastro-oesophageal reflux disease is unclear. AIM: To correlate physician and patient assessment of gastro-oesophageal reflux disease severity and its response to treatment. METHODS: Adult uninvestigated gastro-oesophageal reflux disease patients (n = 217) completed symptom and health-related quality of life questionnaires at baseline and after treatment with esomeprazole 40 mg p.o. daily. Pearson coefficients quantified correlations between physician assessments and patient responses. RESULTS: At baseline, the strongest correlations were heartburn severity (0.31), overall symptom severity (0.44) and a domain of the quality of life in reflux and dyspepsia questionnaire (0.31) (P < 0.001). Correlations of change with treatment were greater than baseline correlations: heartburn (0.39), overall symptoms (0.50) and global rate of change -- stomach problems (0.72, all P < 0.001). The mean difference between the physicians' assessment of change and the patients' global rating of change was 0.20 (95% confidence intervals: 0.10-0.29) with physicians overestimating benefit. CONCLUSIONS: Correlations were often significant, although weak to moderate and better with symptom severity than with health-related quality of life instruments as well as with change after therapy than at baseline. Increasing attention to health-related quality of life may help physicians better understand patients' experience. In clinical trials, treatment success should be assessed by the patient as well as the physician.
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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 | 0.009 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".