Validation of a 7‐point Global Overall Symptom scale to measure the severity of dyspepsia symptoms in clinical trials
Bibliographic record
Abstract
BACKGROUND: Currently there is no consensus on the optimal method to measure the severity of dyspepsia symptoms in clinical trials. AIM: To validate the 7-point Global Overall Symptom scale. METHODS: The Global Overall Symptom scale uses a 7-point Likert scale ranging from 1 = no problem to 7 = a very severe problem. Validation was performed in two randomized-controlled trials (n = 1121 and 512). Construct validity: Global Overall Symptom was compared with the Quality of Life in Reflux And Dyspepsia, Gastrointestinal Symptom Rating Scale, Reflux Disease Questionnaire and 10 specific symptoms using Spearman correlation coefficients. Test-retest reliability: The Intraclass Correlation Coefficient was calculated for patients with stable dyspepsia defined by no change in Overall Treatment Effect score over two visits. Responsiveness: effect size and standardized response mean were also calculated. RESULTS: Construct validity: Change in Global Overall Symptom score correlated significantly with Quality of Life for Reflux And Dyspepsia, Gastrointestinal Symptom Rating Scale, Reflux Disease Questionnaire and specific symptoms (all P < 0.0002). Reliability: The Intraclass Correlation Coefficient was 0.62 (n = 205) and 0.42 (n = 270). Responsiveness: There was a positive correlation between change in Global Overall Symptom and change in symptom severity. The effect size and standardized response mean were 1.1 and 2.1, respectively. CONCLUSION: The Global Overall Symptom scale is a simple, valid outcome measure for dyspepsia treatment trials.
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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 | 0.384 | 0.399 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".