Data Mining Approach for the Characterization of Functional Bowel Disorders According to Symptom Intensity Provides a Small Number of Homogenous Groups
Bibliographic record
Abstract
BACKGROUND/AIMS: The aim of the present study is to evaluate if the intensity of the cardinal symptoms of functional bowel disorders could be used to identify homogenous groups of patients defined by the Rome criteria. METHOD: In this observational study, 1,729 consecutive outpatients (73% females) filled out the Rome III questionnaire and 10-point Likert scales for constipation, diarrhea, bloating (BL)/distension, abdominal pain (AP) during the week before the medical consultation. A Gaussian mixture model was used for clustering the patients according to the intensity of symptoms without a priori information, and a classification tree was constructed from this clustering. Data were analyzed using analysis of variance and logistic regression analysis. RESULTS: According to the intensity of symptoms, the patients are divided into 8 groups named according to their main symptomatology: "painful constipation" (PFC), "mild pain constipation" (MPC), "painful diarrhea" (PFD), "mild pain diarrhea" (MPD), "mixed transit" (MT), "BL," "AP," and "nonspecific" (NS). The study of the relationship between the Rome III classification and this new grouping shows that irritable bowel syndrome (IBS)-constipation is associated with PFC, IBS-diarrhea with PFD and MPD, SII-mixed with MT, SII-unspecified with BL, functional constipation with PFC and MPC, functional diarrhea with MPD and NS, BL with "BL" and NS, nonspecific functional bowel disorders (FBD) with NS, and functional AP with "BL" and AP (p < 0.01 for all associations). CONCLUSION: A symptom intensity-based classification of FBD patients could simplify clinical phenotype, give homogeneous groups of patients, and could eventually be used by nongastroenterologists and in clinical research.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".