Assessing Medication Adherence in Solid-Organ Transplant Recipients
Bibliographic record
Abstract
OBJECTIVES: We sought to determine and compare the prevalence of nonadherence in lung, kidney, and liver transplant recipients, and identify potential risk factors for nonadherence. MATERIALS AND METHODS: This cross-sectional, single-center, retrospective cohort study, evaluated 225 outpatient lung, kidney, and liver transplant recipients' adherence to immunosuppressant medication. Based on immunosuppressant dosages and dispensing records, medication possession ratio (days of medication supplied/actual days) and gaps in prescription refills (> 30-day lapse between expected depletion of supply and next refill) were used as surrogate markers in assessing adherence for 2 years. Patients were adherent to their immunosuppressant medication regimens if their medication possession ratio was ≥ 80%. RESULTS: The mean age of the subjects was slightly greater than 50 years of age, and they were a median of 2.0, 1.3, and 1.1 years posttransplant at the start of data collection for lung, kidney, and liver recipients. Overall medication possession ratios were 95.4% ± 7.5%, 95.9% ± 7.6%, and 92.7% ± 12.3% in our lung, kidney, and liver recipients. Only 7.1% of patients had a medication possession ratio lower than 80%, which was the cutoff for nonadherence. No statistical analyses were performed to identify potential factors for nonadherence because of the small number of nonadherent patients. CONCLUSIONS: Immunosuppressant medication adherence at our center was high for all 3 organ cohorts, as measured by a medication possession ratio of 80% or better. Further study is needed to evaluate immunosuppressant adherence over time after transplant, and confirm the clinical factors that optimize adherence in high-risk patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".