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Enregistrement 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 sur OpenAlexaff
Ingunn Westerheim, Frank Rauch, Tracy Hart, Lena Lande Wekre, Taco van Welzenis, Cathleen Raggio, Heather Mulhall, Alysia Battersby, Samantha J. Prince, Oliver Semler

Notice bibliographique

RevueAdvances in Therapy · 2025
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueConnective tissue disorders research
Établissements canadiensMcGill University
Organismes subventionnairesMereo BioPharma
Mots-clésOsteogenesis imperfectaRheumatologyQuality of life (healthcare)MEDLINEHealth careEmpathyAlternative medicine

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,245
Score d'incertitude au seuil0,327

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,409
Écart entre enseignants0,380 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission1
Résumé présentoui

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