Association between day of the week and medication adherence among adolescent and young adult kidney transplant recipients
Bibliographic record
Abstract
Disruption of usual routines may hinder adherence, increasing the risk of rejection. We aimed to compare weekend versus weekday medication adherence among adolescent and young adult kidney transplant recipients, hypothesizing poorer adherence on weekends. We examined data from the Teen Adherence in Kidney transplant Effectiveness of Intervention Trial (TAKE-IT). We assessed the 3-month run-in period (no intervention) and the 12-month intervention interval, considering a potential interaction between weekend/weekday and treatment group. Adherence was monitored using electronic pillboxes in participants 11-24 years followed in eight transplant centers in Canada and the United States. We used logistic regression with generalized estimating equations to estimate the association between weekends/weekdays and each of perfect taking (100% of prescribed doses taken) and timing (100% of prescribed doses taken on time) adherence. Taking (OR = 0.72 [95% CI 0.65-0.79]) and timing (OR = 0.66 [95% CI 0.59-0.74]) adherence were poorer on weekends than weekdays in the run-in (136 participants) and the intervention interval (taking OR = 0.74 [0.67-0.81] and timing OR = 0.71 [95% CI 0.65-0.77]). There was no interaction by treatment group (64 intervention and 74 control participants). Weekends represent a disruption of regular routines, posing a threat to adherence. Patients and families should be encouraged to develop strategies to maintain adherence when routines are disrupted. TAKE-IT registration number: Clinicaltrials.gov registration: NCT01356277 (May 17, 2011).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".