Does Type of Pain Predict Pain Severity Changes in Individuals With Multiple Sclerosis? A Longitudinal Analysis Using Generalized Estimating Equations
Bibliographic record
Abstract
Background & Objective: Pain is a common symptom among people with MS. In the majority of MS patients, pain is chronic in nature, but it can change over time. The objective of this study was to determine if pain type can predict pain severity changes in individuals with MS over time. Materials & Methods: The research method was a longitudinal design that evaluated pain type and severity at baseline and after 3 years of follow up among people with MS. At the beginning of the study a random sample comprising of 188 individuals with MS were recruited. From those, 78 individuals experienced pain included the study. The McGill pain questionnaire and ID-Pain questionnaire were used to assess type of pain. Numeric Rating Scale was used to measure pain severity. McNemar, Cohen's unweighted Kappa Coefficient, Paired Student t-tests and Generalized Estimating Equations were used to analyze the data.
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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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".