Impact of the Kidney Score Platform on Communication About and Patients’ Engagement With Chronic Kidney Disease Health: Pre–Post Intervention Study
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) affects 14% of the US adult population, yet patient knowledge about kidney disease and engagement in their kidney health is low despite many CKD education programs, awareness campaigns, and clinical practice guidelines. Objective: We aimed to examine the impact of the Kidney Score Platform (a patient-facing, risk-based online tool that provides interactive health information tailored to an individual's CKD risk plus an accompanying clinician-facing Clinical Practice Toolkit) on individual engagement with CKD health and CKD communication between clinicians and patients. Methods: We conducted a pre-post intervention study in which English-speaking veterans at risk for CKD in two primary care settings interacted with the Kidney Score platform's educational modules and their primary care clinicians were encouraged to review the Clinical Practice Toolkit. The impact of the Kidney Score on the Patient Activation Measure (the primary outcome), knowledge about CKD, and communication with their clinician about kidney health was determined with paired t tests. Multivariable linear and logistic models were used to determine whether changes in outcomes after versus before intervention were influenced by age, race or ethnicity, sex, and diabetes status, accounting for baseline values. Results: The study population (n=76) had a mean (SD) age of 64.4 (8.2) years, 88% (67/76) was male, and 30.3% (23/76) self-identified as African-American. Approximately 93% (71/76) had hypertension, 36% (27/76) had diabetes, and 9.2% (7/76) had CKD according to the laboratory criteria but without an ICD-10 (International Classification of Diseases, 10th Edition) diagnosis. Patient interaction with the Kidney Score did not change the mean Patient Activation Measure (preintervention: 40.7%, postintervention: 40.2%, P=.23) but increased the mean CKD knowledge score (preintervention: 40.0%, postintervention 51.1%, P<.01), and changed the percentage of veterans who discussed CKD with their clinician (preintervention: 12.3%, postintervention: 31.5%, P<.01). Changes did not differ by age, sex, race, or diabetes status. Results were limited by the small sample size due to low recruitment and minimal clinician engagement with the Clinical Practice Toolkit during the COVID-19 pandemic. Conclusions: One-time web-based tailored education for patients can increase CKD knowledge and encourage conversations about kidney health. Increasing patient activation for CKD management may require multilevel, longitudinal interventions that facilitate ongoing conversations about kidney health between patients and clinician teams.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".