An Artificial Neural Network Approach for Predicting Functional Outcome in Fibromyalgia Syndrome after Multidisciplinary Pain Program
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate the ability of artificial neural networks (ANNs) to predict, on the basis of clinical variables, the response of persons with fibromyalgia syndrome (FMS) to a standard, 4-week interdisciplinary pain program. DESIGN: The design of this study is retrospective longitudinal. SETTING: Fibromyalgia outpatient clinic in a tertiary-care general hospital. SUBJECTS: The subjects of this study include outpatients with FMS. INTERVENTION: Multidisciplinary pain program including pain pharmacotherapy, cognitive-behavioral therapy, physical therapy, and occupational therapy. OUTCOME MEASURES: Reliable change (RC) of scores on the Stanford Health Assessment Questionnaire (HAQ), and accuracy of ANNs in predicting RC at discharge or at 6-month follow-up as compared to Logistic Regression. RESULTS: ANN-based models using the sensory-discriminative and affective-motivational subscales of the McGill Pain Questionnaire, the HAQ disability index, and the anxiety subscale of Hospital Anxiety and Depression Scale at baseline as input variables correctly classified 81.81% of responders at discharge and 83.33% of responders at 6-month follow-up, as well as 100% of nonresponders at either evaluation time-point. Logistic regression analysis, which was used for comparison, could predict treatment outcome with accuracies of 86.11% and 61.11% at discharge and follow-up, respectively, based on baseline scores on the HAQ and the mental summary component of the Medical Outcomes Study-Short Form 36. CONCLUSIONS: Properly trained ANNs can be a useful tool for optimal treatment selection at an early stage after diagnosis, thus contributing to minimize the lag until symptom amelioration and improving tertiary prevention in patients with FMS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".