Explanation of somatic symptoms by mental health and personality traits: application of Bayesian regularized quantile regression in a large population study
Bibliographic record
Abstract
BACKGROUND: Somatic syndrome is one of the remarkably prevalent issues in primary health care and subspecialty settings. We aimed to elucidate multidimensional associations between somatic symptoms with major mental problems and personality traits in the framework of the quantile regression model with a Bayesian approach. METHODS: A total of 4763 employees at Isfahan University of Medical Sciences and Health Services in Isfahan province, Iran, filled out four validated questionnaires including Hospital Anxiety and Depression Scale (HADS), NEO Questionnaire, General Health Questionnaire (GHQ) and PHQ-15 for somatic symptom severity. In addition, Functional Gastrointestinal Disorders (FGIDs) were determined using Rome IV criteria. Exploratory Factor Analysis (EFA) and Bayesian regularized quantile regression with adaptive LASSO penalization were applied for reduced dimension of somatic symptoms and variable selection and parameter estimation, respectively. RESULTS: The 25 major somatic symptoms were grouped into four factors including general, upper gastrointestinal, lower gastrointestinal and respiratory by EFA. Stress, depression, and anxiety had significant effects on all of the four extracted factors. The effect of anxiety in each four extracted factors was more than stress and depression. Neuroticism and agreeableness had significant effects on all of the four extracted factors, generally (p < 0.05). CONCLUSIONS: Given the high prevalence of somatic symptoms and psychosomatic complaints in correlation with the diverse range of mental co-morbidities, developing more detailed diagnostic tools and methods is crucial; nonetheless, it seems that providing better interdisciplinary approaches in general medical practice is groundwork.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".