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Record W4414542678 · doi:10.1007/s12325-025-03372-8

The IMPACT Survey: The Humanistic Impact of Caring for an Individual with Osteogenesis Imperfecta

2025· article· en· W4414542678 on OpenAlexaff
Ingunn Westerheim, Frank Rauch, Tracy Hart, Lena Lande Wekre, Taco van Welzenis, Cathleen Raggio, Heather Mulhall, Alysia Battersby, Samantha J. Prince, Oliver Semler

Bibliographic record

VenueAdvances in Therapy · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsMcGill University
FundersMereo BioPharma
KeywordsOsteogenesis imperfectaRheumatologyQuality of life (healthcare)MEDLINEHealth careEmpathyAlternative medicine

Abstract

fetched live from OpenAlex

The IMPACT Survey (“IMPACT”) investigated the humanistic, clinical, and economic impact of osteogenesis imperfecta (OI) on affected individuals, caregivers, and the broader community. Prior publications reported the methodology, initial findings, and economic and humanistic impacts on adults with OI. Here, data is presented on the humanistic impact of OI on caregivers to explore how caring for an individual with OI impacts their quality of life (QoL), and any drivers of impact. We hypothesise that caring for an individual with OI will impact the QoL of caregivers. IMPACT, fielded July through September 2021 in eight languages, targeted adults and adolescents with OI, close relatives, and caregivers with or without OI. The survey covered demographics, socioeconomic factors, clinical characteristics, treatment patterns, QoL, and health economics. The impact of caring for an individual with OI was measured across QoL and worry domains. We performed descriptive analyses of the QoL of caregivers without OI and exploratory regression analyses to identify independent associations between caregiver and care recipient characteristics (“predictors”), and their QoL impact on caregivers. Of 528 caregivers without OI with one care recipient, across 10 areas and three domains (career and finances, social and relationships, and mental and physical well-being), 58–83% reported that caring for an individual with OI negatively impacted their QoL; 80% and 83% reported impacted mental health and time for leisure activities, respectively. Predictors of QoL impact included caregiver age, care recipient OI severity, age, and clinical signs, symptoms and events (SSEs). Additionally, 36–96% worried about their care recipients’ future lives, medication access, and transition to adult care. Our results suggest that caring for individuals with OI might have considerable impacts on QoL and worries. The level of impact may be predicted by caregiver age and care recipient OI severity, age, and clinical SSEs. The IMPACT Survey looked at how osteogenesis imperfecta, a rare bone condition also known as brittle bone disease, affects people living with the condition and their caregivers. This report presents data on how caring for someone with osteogenesis imperfecta affects caregivers’ quality of life. The Survey was done in 2021 and was open to caregivers (both with and without osteogenesis imperfecta), as well as people with osteogenesis imperfecta. It asked about health, daily life, treatments, emotional well-being, and caregiving costs. It included 528 caregivers who do not have osteogenesis imperfecta and who care for one person with the condition. Most caregivers (between 58% and 83%) said that caring for someone with osteogenesis imperfecta negatively affected their lives. They reported effects on their mental health, free time, work, social life, and finances. Many also said that they were very worried, especially about the future of their care recipients. For example, they were concerned about accessing medication and moving from child to adult healthcare. Some factors, like the caregiver’s age and the care recipient’s osteogenesis imperfecta severity, were linked to how much the caregiver’s life was affected. This study shows that caring for someone with osteogenesis imperfecta can have a big impact on the caregiver’s life—especially their mental health and personal time.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.409
Teacher spread0.380 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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