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Record W7020954915

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

2023· dissertation· en· W7020954915 on OpenAlexaboutno aff

Bibliographic record

VenueUtrecht University Repository (Utrecht University) · 2023
Typedissertation
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationDiseaseRisk factorSkeletal muscleLimitingIdentification (biology)
DOInot available

Abstract

fetched live from 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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.016
GPT teacher head0.281
Teacher spread0.265 · 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.

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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