Reported pain in multiple sclerosis (MS) and its relationship with affect and attention
Bibliographic record
Abstract
Pain is an important part of MS symptomatology. Studies, with other populations, suggest distress is associated with pain. However, models of the influence of psychological factors on have not been carefully applied and tested with the MS population. The hypothesis was: many patients do not classify much of their sensory disturbance as due to their conceptual framework and this may affect the relationship between and distress. A model of these factors was developed for MS patients. A clinic sample of MS patients, expected to have varying degrees of subjective pain, was recruited. Standard, adapted and new measures were used to characterise the population along the following dimensions: pain, level of cognitive ability (general intelligence and working memory) and cognitive bias, mood, and coping styles. Amount of distress was assessed using a semantic differential measure of wellbeing/distress, Survey of Pain Attitudes and Coping with MS Scale. A Pain Discomfort Scale was adapted to discern differences between people reporting pain versus those reporting discomfort. Pain cognitive-processing bias was explored using assessments including a stem completion task, an experimental recall task using and illness words and a restructured Hayling sentence completion task. Power calculations showed that with 100 patients a detectable correlation would be 0.28 (p=0.05, power = 80%). Measures were compared using paired t-tests for repeated measures, independent t-tests for measures across patients, and regression modelling. McGill adjectives chosen were similar across both high and low responders. Participants reporting pain experienced significantly greater physical impact of MS whereas participants reporting discomfort experienced greater emotional distress. Cognitive bias towards pain, illness and MS related material was not linked with overall or disease state but with coping styles. A model of how emotional stressors affect reported in MS was created.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".