Classification of adults suffering from typical gastroesophageal reflux disease symptoms: contribution of latent class analysis in a European observational study
Bibliographic record
Abstract
BACKGROUND: As illustrated by the Montreal classification, gastroesophageal reflux disease (GERD) is much more than heartburn and patients constitute a heterogeneous group. Understanding if links exist between patients' characteristics and GERD symptoms, and classify subjects based on symptom-profile could help to better understand, diagnose, and treat GERD. The aim of this study was to identify distinct classes of GERD patients according to symptom profiles, using a specific statistical tool: Latent class analysis. METHODS: An observational single-visit study was conducted in 5 European countries in 7700 adults with typical symptoms. A latent class analysis was performed to identify "latent classes" and was applied to 12 indicator symptoms. RESULTS: On 7434 subjects with non-missing indicators, latent class analysis yielded 5 latent classes. Class 1 grouped the highest severity of typical GERD symptoms during day and night, more digestive and non-digestive GERD symptoms, and bad sleep quality. Class 3 represented less frequent and less severe digestive and non-digestive GERD symptoms, and better sleep quality than in class 1. In class 2, only typical GERD symptoms at night occurred. Classes 4 and 5 represented daytime and nighttime regurgitation. In class 4, heartburn was also identified and more atypical digestive symptoms. Multinomial logistic regression showed that country, age, sex, smoking, alcohol use, low-fat diet, waist circumference, recent weight gain (>5 kg), elevated triglycerides, metabolic syndrome, and medical GERD treatment had a significant effect on latent classes. CONCLUSION: Latent class analysis classified GERD patients based on symptom profiles which related to patients' characteristics. Although further studies considering these proposed classes have to be conducted to determine the reproducibility of this classification, this new tool might contribute in better management and follow-up of patients with GERD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".