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 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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".