Health-related quality of life in individuals with syndromic autism spectrum disorders
Bibliographic record
Abstract
ABSTRACT BACKGROUND Children with autism have a significantly lower quality of life compared with their neurotypical peers. While multiple studies have quantified the impact of autism on health-related quality of life (HRQoL) through standardized surveys such as the PedsQL, none have specifically investigated the impact of syndromic autism spectrum disorder on children’s HRQoL or on family quality of life. Here we evaluate HRQoL in children diagnosed with three syndromic Autism Spectrum Disorders (ASDs): Phelan-McDermid syndrome (PMD), Rett syndrome (RTT), and SYNGAP1 -related intellectual disability ( SYNGAP1 -ID). METHODS A standardized online Pediatric Quality of Life Inventory (PedsQL 4.0) survey and the Beach Center Family Quality of Life Scale (FQOL) were administered to caregivers of children with PMD (n= 213), RTT (n= 148), and SYNGAP1 -ID (n= 30). The PedsQL 4.0 measures health-related quality of life in four dimensions: physical, emotional, social and school. The Beach Center Family Quality of Life Scale measures five dimensions: family interaction, parenting, emotional well-being, physical/material well-being and disability-related support. RESULTS For the PedsQL, the most severely impacted dimension in children with syndromic autism was physical functioning. In comparing individual dimensions among the genetically-defined syndromic autisms, individuals with RTT had significantly worse physical functioning, emotional and school scores than PMD. This finding is congruent with the physical regression typically associated with Rett syndrome. Strikingly, syndromic autism results in worse quality of life than other chronic disorders including idiopathic autism. CONCLUSIONS The reduced HRQoL for children with syndromic autism spectrum disorders relative to other chronic childhood illnesses, likely reflects their lack of targeted therapies. This study demonstrates the utility of caregiver surveys in prioritizing phenotypes, which may be targeted as clinical endpoints for genetically defined ASDs. CONTRIBUTORS’ STATEMENT Dr. Bolbocean conceptualized and designed the study, designed the data collection instrument, collected data, performed data analysis, wrote and edited the manuscript. Ms. Andujar performed initial data analysis, drafted the initial manuscript and edited the manuscript. Ms. McCormack performed data analysis and edited the manuscript. Dr. Suter conceptualized and designed the study and made critical edits to the manuscript. Dr. Holder conceptualized and designed the study, designed the data collection instrument, performed data analysis, wrote and edited the manuscript. Table of contents summary In this study, we determine the impact of genetically-defined syndromic autism spectrum disorders on their health-related quality of life. What’s known on this subject Children with neurodevelopmental disorders, including autism, have severely impaired health-related quality of life. Systematic measurement of HRQoL in children with neurodevelopmental disorders through standardized instruments provides a holistic understanding of disease impact and therapeutic endpoint for clinical trials. What this study adds This study defines the impact of three genetically defined autism spectrum disorders: Rett syndrome, Phelan-McDermid syndrome and SYNGAP1 -related Intellectual Disability, on health-related quality of life. We find significantly greater impairment for syndromic ASDs than other neurodevelopmental disorders.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".