Quality of life in children with chronic kidney disease—patient and caregiver assessments
Bibliographic record
Abstract
BACKGROUND: Children with chronic kidney disease (CKD) require strict dietary and lifestyle modifications, however, there is little information on their quality of life. Our objective was to compare health-related quality of life (HRQOL) in children with different stages of CKD to each other and to a control population. METHODS: A cross-sectional assessment of HRQOL for physical, emotional, social and school domains was performed using the PedsQL Generic Core Scale. Data were collected from 20 children with chronic renal insufficiency (CRI; creatinine > 200 micromol/l), 12 on maintenance haemodialysis or peritoneal dialysis (DIAL) and 27 with renal transplants (TX). Caregiver proxy reports were obtained for CRI (n = 20), DIAL (n = 17) and TX (n = 21). Between-group differences were assessed with ANOVA for the CKD groups; t-tests compared our CKD samples with controls. RESULTS: Children with CKD scored lower than the controls in all subscales, however, only TX compared with controls was significant (P < 0.02). DIAL children scored equal to or higher than the TX group in all domains. Analysis of covariance with number of medications as covariate yielded a significant result for the physical subscale (F = 8.95, df = 3, 53, P = 0.004). Proxy caregiver scores were lower than patient scores in all four domains. CONCLUSIONS: Children with CKD rate their HRQOL lower than the healthy controls do. It may be reassuring to caregivers that children on dialysis rate their HRQOL higher than would be expected. However, it is of some concern that caregiver perception of improved HRQOL following transplantation was not shared by their children in the present 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".