A Multifaceted Intervention to Improve Medication Adherence in Kidney Transplant Recipients: An Exploratory Analysis of the Fidelity of the TAKE IT Trial
Bibliographic record
Abstract
BACKGROUND: Inadequate adherence to prescribed immunosuppressive medication regimens among kidney transplant recipients is common, yet interventions are needed to support patients in sustaining adequate adherence to prescribed regimens and achieving optimal transplant outcomes. OBJECTIVE: We examined the preliminary fidelity of a transplant center-based, multifaceted adherence monitoring strategy known as TAKE IT. METHODS: The TAKE IT strategy includes: (1) routine, online, monthly patient self-report adherence assessments; (2) care alerts directed to nurses; (3) quarterly reports monitoring tacrolimus values and adherence trends; (4) support tools tailored to specific adherence concerns. A 2-arm, patient-randomized trial is underway at two large transplant centers (N=449). To evaluate the initial fidelity of TAKE IT, we investigated patient uptake of monthly adherence assessments during the course of a 3-month period, whether any disparities emerged, and the nature of any reported adherence concerns. RESULTS: Among 202 patients randomized and exposed to TAKE IT for 3-months or more, 81% (164/202) completed an adherence assessment, 73% (148/202) completed at least two, and 57% (116/202) completed all monthly assessments. Overall, 50% (82/164) of kidney transplant recipients reported at least one adherence concern over the 3-month assessment period. The most common barriers were classified as regimen-related (eg, regimen complexity), cognitive (eg, forgetfulness), and medical (eg, side effects). Higher-income participants were more likely to complete all surveys compared to lower-income participants (P=.01). CONCLUSIONS: TAKE IT demonstrated 81% (164/202) completion of an adherence assessment, 73% (148/202) completion of at least two, and 57% (116/202) completion of all monthly assessments during this brief, initial observation period. Among those that did respond to the online assessments, the majority demonstrated sustained engagement. Additional monitoring modalities could also be offered to meet patient preferences to ensure all patients' medication use can be properly monitored. TRIAL REGISTRATION: ClinicalTrials.gov NCT03104868; https://clinicaltrials.gov/ct2/show/NCT03104868.
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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 | 0.036 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".