Disease-Modifying Therapies and Adherence in Multiple Sclerosis: Comparing Patient Self-Report with Pharmacy Records
Bibliographic record
Abstract
BACKGROUND: Self-report and pharmacy records are often used to measure adherence rates to disease-modifying therapies (DMTs) in multiple sclerosis (MS), but little is known about how the sources compare. OBJECTIVE: Compare self-report and pharmacy records for assessing DMT use and adherence rates. METHODS: Demographic information, self-reported DMT use, and missed DMT doses in the previous 30 days were obtained from consecutive MS patients attending an MS clinic and linked to pharmacy records. A medication possession ratio (MPR) was calculated using pharmacy records for the year before and after the visit; MPR <80% defined nonadherence. Agreement between self-report and pharmacy records was assessed using Cohen's kappa (κ). RESULTS: Of 326 participants, 135 reported using an injectable DMT. There was near-perfect and perfect agreement between self-report and pharmacy records for DMT use (κ = 0.95; 95% CI 0.91-0.98) and DMT agent (κ = 1.00). Nonadherence was estimated at 13% (17/128) from the 30-days self-report compared to 30% (34/113) and 43% (53/123) in the year pre- and post-clinic visit from pharmacy records, indicating moderate to fair agreement (year prior: κ = 0.41; 95% CI 0.22-0.59; year post: κ = 0.22; 95% CI 0.09-0.36). CONCLUSIONS: Patients self-reports closely reflected pharmacy records when assessing DMT use and product. Moderate to fair agreement was found when comparing adherence rates between sources.
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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 | 0.020 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".