The IMPACT Survey: the humanistic impact of osteogenesis imperfecta in adults
Bibliographic record
Abstract
BACKGROUND: The IMPACT Survey explored the humanistic, clinical, and economic burden of osteogenesis imperfecta (OI) on individuals with OI, their families, caregivers, and wider society. Two previous publications report research methodology, initial insights of the survey, and cost of illness of OI. Here, we present data on the impact of OI on the quality of life (QoL) of adults with OI and explore potential drivers of this impact. METHODS: The IMPACT Survey was an international mixed methods online survey in eight languages (fielded July-September 2021), aimed at adults (aged ≥ 18 years) or adolescents (aged 12-17 years) with OI, caregivers (with or without OI) of individuals with OI, and other close relatives. Survey domains included demographics, socioeconomic factors, clinical characteristics, treatment patterns, QoL, and health economics. We conducted a descriptive analysis of the QoL data, as well as exploratory regression analyses to identify drivers of impact of OI on QoL (independent associations between patient characteristics and the impact on QoL). RESULTS: 1,440 adults with OI participated in the survey. The proportion who reported an impact of OI on their QoL across individual areas in the physical, socioeconomic, and mental well-being domains ranged between 49 and 84%. For instance, 84% of adults reported an impact of OI on the types of leisure activities they could do and 74% on the type of job they could do. More severe self-reported OI and higher fracture frequency were consistently identified as drivers of OI's impact on QoL. The proportion of adults who reported worrying about different aspects of their lives due to their OI, such as mobility loss, future fractures, and ageing, ranged between 31 and 97%. CONCLUSION: IMPACT provides insights into the humanistic burden of OI on adults, revealing that OI has a substantial impact on the QoL of adults. OI severity and fracture frequency were consistently identified as drivers of impact on QoL across all domains. Understanding these drivers may aid in identifying areas for targeted interventions, such as fracture prevention.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".