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.Results: Findings indicated that all pain severity ratings raised on average, though this difference was statistically significant only for lowest pain (P=0.0006).Type of pain did not change in the major part of study sample (P=0.44).Results further indicated that over the follow-up period the lowest pain severity scores were significantly predicted by type of pain (P<0.05), while the pain ratings in worst pain severity was not predicted by the type of pain.Conclusion: Results of the current study help for better understanding of the pain type and severity changes over time among patients with MS.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".