Medication Nonadherence in Systemic Lupus Erythematosus: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Medication nonadherence has not been well characterized in systemic lupus erythematosus (SLE). Our objective was to a conduct a systematic review of the literature, examining the burden and determinants of medication nonadherence in SLE. METHODS: We conducted a systematic search of Medline (1946-2015), Embase (1974-2015), and Web of Science (1900-2015) databases and selected original studies of SLE patients that evaluated nonadherence to SLE therapies as the primary study outcome. We extracted information on study design, sample size, length of followup, data sources, type of nonadherence problem examined, adherence measures and reported estimates, and determinants of adherence reported in multivariable analyses. RESULTS: After screening 4,111 titles, 11 studies met the inclusion criteria. Study sample sizes ranged from 32 to 246 patients, and studies were categorized according to data source: self-report (5), electronic monitoring devices (1), clinical records from rheumatology clinics (3), and refill information from pharmacy records (2). Overall, the percentage of nonadherent patients ranged from 43% to 75%, with studies consistently reporting that over half of patients are nonadherent. Studies also showed that up to 33% of patients discontinue therapy after 5 years. Determinants of nonadherence included having depression, rural residence, lower education level, and polypharmacy. CONCLUSION: Overall, synthesis of current evidence suggests that the burden of medication nonadherence is substantial in SLE. Findings highlight the importance of developing interventions to support adherence and improve outcomes among patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".