Derivation and validation of a novel method to subgroup patients with functional dyspepsia: beyond upper gastrointestinal symptoms
Bibliographic record
Abstract
BACKGROUND: Conventionally, patients with functional dyspepsia are subgrouped based on upper gastrointestinal symptoms, according to the Rome criteria. However, psychological co-morbidity and extraintestinal symptoms are also relevant to functional gastrointestinal disorders. AIM: To investigate whether it is possible to subgroup people with functional dyspepsia using factors beyond upper gastrointestinal symptoms. METHODS: We collected demographic, symptom and psychological health data from adult subjects meeting the Rome III criteria for functional dyspepsia in two secondary care cross-sectional surveys in Canada and the UK. We performed latent class analysis, a method of model-based clustering, to identify specific subgroups (clusters). For each cluster, we drew a radar plot, and compared these by visual inspection, describing cluster characteristics. RESULTS: In total, 400 individuals met Rome III criteria for functional dyspepsia in the Canadian cohort, and 262 the UK cohort. A four-cluster model was the optimum solution and the characteristics of the clusters were almost identical between the two cohorts. The clusters were defined by a pattern of gastrointestinal symptoms and were further differentiated by the extent of extraintestinal and psychological co-morbidity. Cluster 1 (mean age 46.7 years, 66.7% female) consisted of epigastric pain and nausea with high psychological burden, cluster 2 (mean age 41.5 years, 77.7% female) high overall gastrointestinal symptom severity with high psychological burden, cluster 3 (45.8 years, 67.2% female) oesophageal symptoms and early satiety with low psychological burden, and cluster 4 (mean age 40.4 years, 71.5% female) postprandial fullness with low psychological burden. We validated the model derived using the Canadian study population externally by applying it to the UK dataset. We demonstrated reproducibility; it would perform similarly when applied to a different dataset. CONCLUSIONS: Latent class analysis identified four distinct functional dyspepsia subgroups characterised by varying degrees of gastrointestinal symptoms, extraintestinal symptoms and psychological co-morbidity. Further research is needed to assess whether they might be used to direct treatment.
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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 | 0.023 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".