37 Adherence to treatment in Multiple Sclerosis. The importance of personality, executive functions, and social support
Bibliographic record
Abstract
Objective: To test whether adherence to treatment in patients with MS is influenced by cognitive variables (executive functions), personality, and social support. Participants and Methods: This is a pilot observational, descriptive, cross-sectional study. 60 patients with Relapsing remitting MS ( 73.33% female; age: 41.41 ±14.00) undergoing medical treatment ( 28 dymethilfumarate, 7 ocrelizumab/ rituximab, 6 fingolimod, 5 interferon, 5 natalizumab, 4 cladribine, 3 teriflunomide, 1 alemtuzumab, 1 glatiramer acetate) underwent a comprehensive multi-component evaluation including : cognition, social support (using the self-reported record of social support scale), personality (using the NEO-FFI questionnaire) and evaluation of treatment adherence using the Morisky Green Levine Medication Adherence Scale Participants were divided into two groups according to their adherence to medical treatment, low vs. high adherence was defined using a cutoff score of 4. Differences between groups were evaluated using Student's t-test with a significance level of p<0.05, the effect size was calculated with Cohen's d test. Results: Groups did not differ significantly in age, sex, type of treatment, Montreal Cognitive Assesments (MoCA) or neuropsychiatric scales of depression and anxiety. Regardless of treatment type, 63.33% of the patients had high treatment adherence. Significant differences between groups were found in the Global Index of Social Support (p=0.016, Cohen's d= 0.73) and the responsibility factor of the NEO-FFI (p=0.048, Cohen's d= 0.20). Conversely, no significant differences were found in executive functions (p=0.8), Openness (p=0.062), Extraversion (p=0.5), Neuroticism (p=0.4) and Agreeableness (p=0.8). Conclusions: Social support and the responsibility factor of personality are significantly different between MS patients with high and low adherence to medical treatment. The study of social support and personality may be a key component in improving adherence strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".