Feasibility and acceptance of KIDSCREEN-52 as a screening tool for unmet needs in children with rare inflammatory diseases
Bibliographic record
Abstract
Abstract Background Children living with rare diseases often face significant psychosocial challenges; recognizing and addressing these effectively is crucial. However, there is a paucity of comprehensive screening tools. This study aimed to assess the feasibility and acceptance of the comprehensive KIDSCREEN-52 tool in identifying unmet needs of children with rare inflammatory diseases and their caregivers and identifying factors associated with low health-related quality of life (HRQoL). Methods A prospective single-center study of consecutive pediatric patients aged 8–18 with inflammatory diseases and their caregivers was performed to assess HRQoL utilizing the multidimensional KIDSCREEN-52 self-report and proxy tool. The validated KIDSCREEN-52 tool is available in 13 languages with corresponding Norm Data. It captures HRQoL across 10 domains including 52 inquiries. HRQoL of children with rare inflammatory diseases was described utilizing the multidimensional KIDSCREEN-52 self-report and proxy tool. The feasibility and acceptability of KIDSCREEN-52 was determined using a simple, dichotomous three item acceptance tool. Factors associated with low self-reported HRQoL were explored. Results A total of 104 participants, comprising 51 pediatric patients and their 53 caregivers, were included. The patients were 35 females and 16 males, with a median age of 16 years (range: 9–18). Among them, 25 (49%) had autoinflammatory diseases, 26 (51%) had rheumatic diseases. Mean values from self-reports and proxies were consistent with the Norm Data across all domains. Self-report and proxy assessments showed high-degree agreement. Patients reported lower HRQoL levels compared to the control population in nearly all domains. Both caregivers and children expressed strong acceptance of the KIDSCREEN-52 questionnaire's clarity, relevance, and adequacy. The overall completion rate was 75%, the mean completion time 17 min (range: 10–25). Factors associated with low HRQoL included female gender, adolescent age and evidence of a rheumatic disease. Conclusion The KIDSCREEN-52 demonstrated promise as a feasible and accepted tool for capturing the HRQoL and identifying unmet needs in children with rare inflammatory diseases. Its comprehensiveness and the availability in multiple languages with corresponding Norm Data, offers a unique opportunity to implement strategies to identify and address HRQoL challenges of children with rare diseases in routine clinical care.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".