MP846HEALTH-RELATED QUALITY OF LIFE IN CHILDREN WITH CHRONIC KIDNEY DISEASE - INTRODUCTION INTO ROUTINE CARE
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Health-related quality of life (HRQoL) measures a patient’s broader health status. METHODS: Families of children with Chronic Kidney Disease (CKD) were asked to complete both the self-reporting and parent-proxy components of the Paediatric Quality of life Inventory (version 4.0 - English (United Kingdom)) PedsQLTM. RESULTS: 60 families were recruited; 55 of whom were conservatively managed (mean age 10.66 years, range: 3.72 - 17.78 years). Overall scores from this cohort in totality were lower than the previously published control data, but the 55 conservatively managed patient scores were similar to healthy control data, and significantly higher than previously published conservatively managed cohorts form Brazil, Canada and the USA. There was no correlation with estimated glomerular filtration rate (eGFR) or duration of illness and any domain score. A significant disparity was found in the emotional domain between self-reported and parent-proxy questionnaires (p=0.005), but on subgroup analysis by age-group, only remained in those aged 5 - 7 years (p=0.046). Analysis of only those receiving conservative management demonstrated a similar pattern. 10 children had received functioning renal transplant. Comparison between those that had a renal transplant and those that did not, did not demonstrate any difference in scores in the self-rated questionnaire, but demonstrated lower parent-proxy scores in those with a functioning graft. CONCLUSIONS: Children with CKD have lower HRQoL scores than healthy controls, with no correlation with eGFR.These UK data were not concordant with previously published PedsQLTM scores in conservatively managed cohorts. The reasons for this need further exploration, but may be due to different populations receiving different healthcare services, and/or the small, relatively homogenous population of this cohort. Parents tended to score their child lower than the child themselves in the emotional domain. These data contradict previous literature that suggests that discordance should increase with the age.HRQoL tools have the potential to be used to evaluate services, as screening tools, and to identify areas to be explored during consultation. HRQoL assessment should be considered for introduction into routine clinical care for ongoing holistic care of children with chronic illnesses, including CKD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".