Symptom-based ordinal scale fuzzy clustering of functional gastrointestinal disorders
Bibliographic record
Abstract
Background The validity of Rome III criteria for diagnosing functional gastrointestinal disorders (FGIDs) have been frequently questioned in the literature. In epidemiology, when a disease is diagnosed, the existence of a true cluster must be proven. Thus, clustering the common GI symptoms of individuals and comparing the clusters with FGIDs defined by the Rome III criteria could provide insights about the validity of FGIDs defined by those criteria. Well-separated compact clusters were detected in responses to questionnaires of the epidemiological features of different FGIDs in Iranian adults using fuzzy ordinal clustering. The representative sample from each cluster i.e. Cluster Representative (CR) was formed whose corresponding FGID was diagnosed with Rome III criteria. Then, FGID diagnosis was performed for all participants in each cluster and the percentage of cases whose FGID was the same as the cluster's identified FGID (agreement) was reported. Results Fourteen valid clusters were detected in 4763 people. The average membership of the objects in each cluster was 77.3%, indicating similarity of the objects in clusters to their corresponding CRs. Eight clusters were assigned to single FGIDs (irritable bowel syndromes: constipation IBS-C, diarrhea IBS-D and un-subtyped IBS-U; functional bloating FB; functional constipation FC; belching disorder BD. The agreement was higher than 50% in single FGID clusters except those whose diagnosis was IBS-U. Conclusions IBS-C, IBS-D, FC, BD, and FB defined with Rome III criteria exist in the population, which is not the case for IBS-U.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".