Assessing Risk Factors and Posttransplant Outcomes of Nonadherence Among Kidney Transplant Recipients
Bibliographic record
Abstract
Background: Adherence of kidney transplant recipients (KTRs) to prescribed regimens is vital for long-term graft function. This study aimed to identify adherence rates using objective and composite measures, risk factors for nonadherence, and the latter's impact on posttransplant outcomes. Methods: A retrospective single-center cohort study was conducted among KTR transplanted from January 1, 2003, to December 31, 2017. Overall nonadherence was defined as 1 or more of the following in the first-year posttransplant: (1) at least 1 missed clinic visit, (2) >30% missed laboratory visits, and (3) >40% coefficient of variation of calcineurin inhibitor levels. Logistic and Cox proportional hazards models were fitted to identify adherence risk factors and outcomes, respectively. Results: Among the included 1803 KTR, overall nonadherence was identified in 34.9%; 11.2% were nonadherent to clinic visits, 5.4% to laboratory tests, and 25.2% to medications. Recipient history of psychiatric disorders (odds artio [OR], 1.57; 95% confidence interval [CI], 1.22-2.02) or pretransplant nonadherence (OR, 1.82; 95% CI, 1.31-2.54), and private drug coverage (OR, 0.62; 95% CI, 0.48-0.80) were associated with posttransplant nonadherence. Any episode of nonadherence over the first year after transplant was associated with an increased risk of total graft failure (hazard ratio [HR], 1.52; 95% CI, 1.20-1.91), death with graft function (HR, 1.51; 95% CI, 1.11-2.05), and biopsy-proven acute rejection (HR, 2.35; 95% CI, 1.38-3.99). Conclusions: Adherence among KTR is influenced by both psychosocial and socioeconomic determinants which impact posttransplant outcomes. Our results emphasize feasible methods to monitor adherence and identify high-risk KTR.
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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) | 0.001 | 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".