Methods for creating a portrait of outcomes in pediatric rare diseases: An example from pediatric Multiple Sclerosis
Bibliographic record
Abstract
It is estimated that more than 55 million people live with a diverse array of diseases that are considered rare. Because of the rarity, little is known about its impact on a child’s life. Families of children with rare diseases are concerned with not having sufficient information about the disease course, treatment options and outcomes. Apart from survival, families are most concerned about the child’s quality of life (QOL). A key challenge is a lack of condition specific QOL measures to quantify the impact of treatment and disease. Instead, generic measures of health-related quality of life (HRQL) are used to infer QOL. The rarity of the disease poses many challenges related to sample size and heterogeneity. To overcome these challenges, integration of multiple data sources is the most feasible option. This approach is called the Multiple Data Integration Approach (MEDIA). Therefore, the overall objective of this PhD thesis is to describe methods for creating a portrait of outcomes of a rare disease from integrating different sources of data, using an example in pediatric MS. As there is currently no condition-specific measure of QOL in pediatric MS, our first step was to develop one. To develop a measure of QOL, a qualitative synthesis was conducted, and a framework of pediatric QOL was purposed (Manuscript 1). When generic measures are used as outcomes, cultural differences can confound the effect of the health condition on the total score. The problem is magnified in rare diseases because participants are often recruited worldwide. A potential solution was to estimate a global score in typically developing children as a reference and estimate the adjustments needed to consider cultural and regional effects (Manuscript 2). A systematic review was also conducted to identify QOL outcome measures in pediatric MS and to estimate a global score (Manuscript 3) among children and adolescents with MS. Results showed scores were the same as typically developing peers. This suggested that MS has minimal impact on a child’s life and raised the question of whether generic measures were capturing the domains of life important to children and adolescents with MS and their families. In Manuscript 4, relevant domains of life were identified through an online survey and the Heck-Laurin Pediatric MS measure was developed. Manuscripts 5 and 6 used existing data. Manuscript 5 used the Multiple Sclerosis Outcomes Assessment Consortium database, with data arising from MS clinical trials. Group-based trajectory modeling was conducted to identify patterns of disability progression in 676 young people with MS. Performance of two groups of people with MS, were compared (18 to 25 years old and 26 to 35 years old) using linear mixed models. Results showed that disability progression was stable, with about 25% of people with impairments in gait and hand function. These results indicated that young people with an earlier onset have a different course of disability progression. Manuscript 6 was from the National Rehabilitation Reporting System on young people with MS who need inpatient rehabilitation. Latent class analysis was conducted to identify the disability profiles of young people with MS at admission to Canadian rehabilitation facilities and discharge. At admission, approximately 20% of young people with MS were incontinent and dependent in mobility and self-care. This proportion was much higher in the younger group (16 to 25 years). The use of MEDIA allowed for the generation of new knowledge on QOL and HRQL, identification of important and relevant long- and short-term disability outcomes in pediatric MS, and a better understanding of disability progression in young people with MS. This thesis illustrated the feasibility of MEDIA and contributed evidence towards solutions for overcoming challenges when conducting research in rare disease populations. MEDIA could be adapted to other rare disease populations to generate new knowledge
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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 | 0.062 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".