Changes in patient activation in people starting dialysis: A prospective longitudinal, observational study
Bibliographic record
Abstract
INTRODUCTION: Increased patient activation is associated with improved health outcomes; however, little is known about patient activation in people with end-stage kidney disease at the start of their dialysis journey. This study aimed to measure activation status changes over the first 4 months of dialysis. METHODS: Prospective, longitudinal, and observational study. Incident patients initiating dialysis at 25 in-center hemodialysis and 17 home dialysis programs across three US states managed by the same dialysis provider completed the 13-item Patient Activation Measure (PAM-13) survey at baseline (month 1 after commencement of dialysis) and follow-up (month 4). The survey yields a score (0-100) that corresponds to four levels (1-4), with higher scores or levels indicating higher activation. FINDINGS: One hundred eighty-two participants (139 center, 43 home) completed both baseline and follow-up surveys. Mean age was 60 ± 15 years, 40% female. Mean PAM-13 scores were 65.1 ± 16.8 and 64.8 ± 17.8 at baseline and follow-up, respectively; mean intraindividual change: -0.3 ± 17.3. The proportions of patients at levels 1-4 at baseline were 11%, 23%, 35%, and 31% respectively. At follow-up, 50%, 64%, 52%, and 37% of participants at levels 1-4, respectively, changed to a different PAM level (Spearman correlation = 0.47; p < 0.001). Home dialysis was associated with higher PAM scores when compared to in-center hemodialysis in multivariable analyses, adjusted for sociodemographic variables, comorbidities, and predialysis nephrology care (β = 5.74, 95% confidence intervals [CI]: 0.11-11.37 and 9.02, 95% CI: 3.03-15.02, at baseline and follow-up, respectively). DISCUSSION: Although aggregated group scores and levels remained stable, intra-individual patient activation changed significantly during the first 4 months of dialysis. This novel finding is foundational to future projects aiming to design interventions to improve patient activation.
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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 | 0.003 | 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 | 0.001 | 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".