A prospective study of adherence to lenalidomide for multiple myeloma using Medication Event Monitoring System (MEMS) caps
Bibliographic record
Abstract
Abstract Purpose In patients with multiple myeloma, characterizing adherence to orally administered therapies, such as lenalidomide, is critical given their frequent use and potential for poorer outcomes associated with nonadherence. However, little data exist using prospective measures of adherence in this population. Our study piloted use of Medication Event Monitoring System (MEMS) caps and the patient-reported Brief Adherence Rating Scale (BARS) for 3 months in older adults with multiple myeloma. Methods We enrolled 13 patients with multiple myeloma receiving lenalidomide. Baseline characteristics were summarized; mean adherence to lenalidomide was reported with 95% confidence intervals. Results The median follow-up was 84 days. Of the 12 participants evaluable, median adherence, as assessed by the MEMS cap data, was 98%. Only 5 had 100% adherence. Deviations from intended use included missed prescribed doses made up during scheduled off week, additional days off between cycles, or taking fewer than anticipated days off. None of these events evident in MEMS data were self-disclosed. The mean difference in adherence estimated between the BARS and MEMS caps was 2%. Conclusion In this small sample, the observed adherence was higher than reported in retrospective studies using Medication Possession Ratio as a proxy for adherence. The BARS can be easily integrated into clinical encounters but has potential for reporting bias. MEMS caps can help characterize patterns of nonadherence, though there are limitations to their utility and the data can require thorough manual review to reconcile suspected occurrences of nonadherence. Studies should use more than 1 complementary measure of adherence. Clinicaltrials.gov ID: NCT03779555 , Registered 12/19/2018
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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 | 0.004 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".