Perceived and Objective Kidney Disease Knowledge in Patients With Advanced CKD Followed in a Multidisciplinary CKD Clinic
Bibliographic record
Abstract
BACKGROUND: One of the key components of multidisciplinary CKD clinics is education; however, kidney disease knowledge among patients followed in these clinics is not routinely measured. OBJECTIVE: The aim of this study was to determine objective and perceived kidney disease knowledge and patient characteristics associated with knowledge among patients followed in a multi-care kidney clinic. DESIGN: This is a cross-sectional survey study. SETTING: This study was conducted in a multi-care kidney clinic in Ontario, Canada. PATIENTS: Patients who did not speak English, who were unable to read due to significant vision impairment, or who had a known history of dementia or significant cognitive impairment were excluded. MEASUREMENTS: Perceived kidney disease knowledge was evaluated using a previously validated 9-item survey (PiKS). Each question on the perceived knowledge survey had 4 possible responses, ranging from "I don't know anything" (1) to "I know a lot" (4). Objective kidney disease knowledge was evaluated using a previously validated survey (KiKS). METHODS: The association between patient characteristics and perceived and objective kidney disease knowledge was determined using linear regression. RESULTS: , respectively. The median (IQR) objective and perceived knowledge survey scores were 19 out of 27 (16, 21) and 2.9 out of 4 (2.4, 3.2), respectively. Only 25% of patients answered correctly that CKD can be associated with no symptoms, and 64% of patients identified correctly that the kidneys make urine. More than 60% of patients perceived themselves to know nothing or only a little about medications that help or hurt the kidney. Older age was independently associated with lower perceived and objective knowledge, but sex, income, and educational attainment were not. LIMITATIONS: This is a single-center study. Cognitive impairment was based on the treating team's informal assessment or prior documentation in the chart; formal cognitive testing was not performed as part of this study. CONCLUSIONS: Despite resource-intensive care, CKD knowledge of patients followed in a multidisciplinary clinic was found to be modest. Whether enhanced educational strategies can improve knowledge and whether increasing knowledge improves patient outcomes warrants further study.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".