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Enregistrement W7020954915

Musculoskeletal vulnerability and physical frailty during and after treatment for childhood cancer

2023· dissertation· en· W7020954915 sur OpenAlexaboutno aff

Notice bibliographique

RevueUtrecht University Repository (Utrecht University) · 2023
Typedissertation
Langueen
DomaineMedicine
ThématiqueChildhood Cancer Survivors' Quality of Life
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPopulationDiseaseRisk factorSkeletal muscleLimitingIdentification (biology)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Yearly, around 600 Dutch children are diagnosed with cancer. Advancements in treatment strategies and supportive care have resulted in a current 5-year survival rate of approximately 83%. However, intensive treatment affects the musculoskeletal system with negative consequences for physical abilities in both the short and long term. This thesis focuses on early identification of musculoskeletal impairments and physical vulnerability in children with cancer, as well as the prevalence of frailty-related impairments and participation ability in survivors of childhood cancer. Children with acute lymphoblastic leukemia (ALL) have an increased risk of sustaining fractures, which is linked to low lumbar spine bone mineral density (LSBMD). To identify patients at risk, we developed prediction models for LSBMD at diagnosis and at the end of treatment using a Dutch multicenter cohort of newly diagnosed ALL patients, and validated these externally in a Canadian multicenter cohort. The models demonstrated accurate predictions by using weight Z-scores and age. Besides the risk of bone deterioration, children with cancer are at risk of muscle impairment, such as sarcopenia. Given the serious consequences of sarcopenia leading to increased infections and disability, it is important to identify at-risk patients early. We examined the diagnostic accuracy of the pediatric version of the SARC-F questionnaire, and demonstrated that it is an excellent tool for identifying patients with sarcopenia. Assessing skeletal muscle mass in children remains challenging due to limited non-invasive methods. In this thesis, we demonstrated that muscle ultrasound is feasible as a non-invasive tool for muscle assessment in children with ALL. Moreover, correlations between muscle size with overall skeletal muscle mass, as well as a relationship between higher intramuscular fat infiltration and reduced muscle function revealed that it may be a valid approach for detecting early muscle deterioration. Prior to this thesis, it was well-known that dexamethasone treatment could lead to muscle wasting, but it had never been studied in children with ALL. We observed a 13.5% increase in the occurrence of frailty following a 5-day dexamethasone course. Importantly, lower muscle function at the onset of a dexamethasone course, seemed predictive of developing frailty following the course. Although frailty in pediatric cancer patients can be attributed to the disease itself and/or as acute side-effects of treatment, frailty has also been recognized as a long-term side effect. In Dutch long-term childhood cancer survivors we observed that frailty occurred. Notably, survivors of acute myeloid leukemia, particularly those who underwent radiation therapy, appeared to be at a higher risk of frailty. All the previous mentioned side effects can have a significant impact on everyday life participation. Specifically, survivors of pediatric brain tumors who have been exposed to cranial radiation and brain surgery. This thesis revealed that over 50% of brain tumor survivors experienced limited participation compared to their age-expected level. Remarkably, these participation limitations did not appear to diminish over time and were associated with physical impairment and reported fatigue. This thesis enhanced the understanding of the musculoskeletal impact of childhood cancer (treatment) and we have made the initial steps towards early identification.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
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 enseignants0,509
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
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 Opus0,016
Tête enseignante GPT0,281
Écart entre enseignants0,265 · 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.

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

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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