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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".