ArticleFrontiers in public health2023
Mapping the Peds QL
Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mapping to estimate health state utility: a systematic review of development and limitations.Health and quality of life outcomes · 2025Pooled it
- Clinical features, disease burden and impact on quality of life in participants with mitochondrial encephalomyopathy.Frontiers in neurology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The study aims to develop a mapping algorithm from the Pediatric Quality of Life Inventory™ 4. 0 (Peds QL 4.0) onto Child Health Utility 9D (CHU-9D) based on the cross-sectional data of functional dyspepsia (FD) children and adolescents in China. Methods: A sample of 2,152 patients with FD completed both the CHU-9D and Peds QL 4.0 instruments. A total of six regression models were used to develop the mapping algorithm, including ordinary least squares regression (OLS), the generalized linear regression model (GLM), MM-estimator model (MM), Tobit regression (Tobit) and Beta regression (Beta) for direct mapping, and multinomial logistic regression (MLOGIT) for response mapping. Peds QL 4.0 total score, Peds QL 4.0 dimension scores, Peds QL 4.0 item scores, gender, and age were used as independent variables according to the Spearman correlation coefficient. The ranking of indicators, including the mean absolute error (MAE), root mean squared error (RMSE), adjusted R Results: The Tobit model with selected Peds QL 4.0 item scores, gender and age as the independent variable predicted the most accurate. The best-performing models for other possible combinations of variables were also shown. Conclusion: The mapping algorithm helps to transform Peds QL 4.0 data into health utility value. It is valuable for conducting health technology evaluations within clinical studies that have only collected Peds QL 4.0 data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.