Markers of Fibromyalgia: Classification and Subtyping Using Self-Reported Measures
Bibliographic record
Abstract
Fibromyalgia (FM) is a chronic pain disorder marked by widespread physical symptoms and psychological comorbidities. While neuroimaging has dominated FM research, such methods are often impractical or inaccessible in routine clinical care. This study investigates whether self-reported features can effectively distinguish FM patients from healthy controls (HC) and identify clinically meaningful FM subtypes, using the Emo-Fibro dataset (N = 66; 33 FM, 33 HC). We trained and evaluated supervised machine learning models including Logistic Regression, Random Forest and Support Vector Machine on the Toronto Alexithymia Scale (TAS-20) and the Positive and Negative Affect Schedule (PANAS), assessing feature importance using absolute coefficients, Gini importance, and permutation importance. The Random Forest model achieved the highest classification performance (Accuracy = 0.81, ROC-AUC = 0.82), indicating that these features alone can offer robust diagnostic insight. We then applied K-Means clustering to the FM group and identified two subtypes: high-distress versus lower-distress, characterized by emotional regulation, psychological burden, and affect. These findings suggest that patient-reported psychological data not only aid FM diagnosis but also reveal meaningful heterogeneity to guide personalized care. By focusing on accessible, self-reported measures, this study supports a practical and emotion-informed approach to FM research and management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".