Utility of the MARS-5 in Assessing Medication Adherence in IBD
Bibliographic record
Abstract
INTRODUCTION: We aimed to validate the Medication Adherence Report Scale-5 (MARS-5) as a tool for assessing medication adherence in inflammatory bowel disease (IBD) and to determine predictors of medication adherence. METHODS: One hundred twelve (N = 112) adults with confirmed IBD participating in the longitudinal Manitoba Living With IBD Study were eligible. Demographics, IBD type, surgeries, disease activity (using the Inflammatory Bowel Disease Symptom Inventory and fecal calprotectin levels), perceived stress, and medication use were collected biweekly through online surveys. The MARS-5 scores were obtained at baseline and at 1 year. Correlation between medication monitoring data and MARS-5 scores was performed and the optimal MARS-5 cutoff point for adherence assessment determined. Predictors of medication adherence were assessed at both ≥90% and ≥80%. RESULTS: Participants were predominantly female (71.4%), mean age was 42.9 (SD = 12.8), and the majority (67.9%) had Crohn disease (CD). Almost half (46.4%) were taking more than 1 IBD medication, with thiopurines (41.9%) and biologics (36.6%) the most common. Only 17.9% (n = 20) were nonadherent at a <90% level; of those, 90% (n = 18) were using oral medications. The MARS-5 was significantly associated with adherence based on medication monitoring data at baseline (r = 0.48) and week 52 (r = 0.57). Sensitivity and specificity for adherence ≥80% and ≥90% were maximized at MARS-5 scores of >22 and >23, respectively. Having CD (OR = 4.62; 95% confidence interval, 1.36-15.7) was the only significant predictor of adherence. CONCLUSION: MARS-5 is a useful measure to evaluate adherence in an IBD population. In this highly adherent sample, disease type (CD) was the only predictor of medication adherence.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".