Frequency and correlates of non-adherence in a large sample of adult patients after kidney transplantation: A cross-sectional KTx360° substudy
Bibliographic record
Abstract
OBJECTIVE: Non-adherence to immunosuppressive medication (ISM) is a common and preventable cause of graft loss after kidney transplantation (KTx). Adherence is a multifaceted construct influenced by various factors. Despite frequent reports linking non-adherence with multiple variables, results often seem contradictory. The lack of patient registers in Germany to encompass psychosocial aspects limits data to smaller, selective studies. The aim was to evaluate the frequency and correlates of non-adherence in a large sample of adult KTx patients. METHODS: This cross-sectional substudy was embedded within the structured follow-up program KTx360°. It took place at the transplant centers of Hannover Medical School, Hann. Münden and Erlangen in Germany between May 2017 and October 2020. 838 adult KTx patients participated in this substudy. Adherence was assessed using the Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS) alongside other sociodemographic, psychosocial, and medical variables. RESULTS: Participants had an average age of 52.3 years (SD = 13.5), with 58.7 % male. According to the BAASIS interview, 22.1 % reported non-adherence, mainly due to timing (13.0 %) and taking (11.7 %) non-adherence, with only 0.5 % ceasing at least one ISM. Factors linked to non-adherence in regression analysis included younger age, male gender, longer post-transplant time, and perceived medication overuse. The effect sizes were small. The informative value is limited by the design and the sample size. CONCLUSION: In our cross-sectional analysis, nearly a quarter of the participants reported suboptimal adherence, mainly associated with primarily non-modifiable risk factors. Routine adherence assessments and targeted interventions are essential to improving long-term outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".