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Record W4366213204 · doi:10.3389/fendo.2023.1082413

Bone health in childhood and adolescence: an overview on dual-energy X-ray absorptiometry scanning, fracture surveillance and bisphosphonate therapy for low-middle-income countries

2023· review· en· W4366213204 on OpenAlexafffund
Dilki Madhuchani, Sumudu Nimali Seneviratne, Leanne M. Ward

Bibliographic record

VenueFrontiers in Endocrinology · 2023
Typereview
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsMedicineOsteoporosisDual-energy X-ray absorptiometryContext (archaeology)Bone mineralBisphosphonateTrabecular bone scorePeak bone massBone densityPhysical therapyPediatricsQuantitative computed tomographyInternal medicine

Abstract

fetched live from OpenAlex

Bone accrual in childhood determines bone health in later life. Loss of bone strength in early life can lead to increased morbidity and reduced quality of life in childhood and adolescence. Increased availability of assessment tools and bisphosphonate therapy, together with increased awareness on the significance of fracture history and risk factors, have led to greater opportunities, to improve detection and optimize management of children and adolescents with bone fragility globally, including those in lower resource settings. Bone mineral density z-scores and bone mineral content are surrogate measures of bone strength, which can be measured by dual-energy X-ray absorptiometry (DXA), in growing individuals. DXA can aid in the diagnosis and management of primary and secondary bone fragility disorders in childhood. DXA helps evaluate children with clinically significant fractures, and monitor those with bone fragility disorders, or at high risk for compromised bone strength. Obtaining DXA images can however be challenging, especially in younger children, due to difficulty in positioning and movement artefacts, while paediatric DXA interpretation can be confounded by effects of growth and puberty. Furthermore, access to DXA facilities as well as appropriate paediatric reference norms and expertise for interpretation, may not be easily available especially in lower resource settings. Pediatric bone experts are now placing increasing emphasis on the fracture phenotype and clinical context to diagnose osteoporosis over bone mineral density (BMD) by DXA. Low trauma vertebral fractures are now recognized as a hallmark of bone fragility, and spinal fracture surveillance by either conventional lateral thoracolumbar radiographs or vertebral fracture assessment by DXA is gaining increasing importance in diagnosing childhood osteoporosis, and initiating bone protective therapy. Furthermore, it is now understood that even a single, low-trauma long bone fracture can signal osteoporosis in those with risk factors for bone fragility. Intravenous bisphosphonate therapy is the mainstay of treatment for childhood bone fragility disorders. Other supportive measures to improve bone strength include optimizing nutrition, encouraging weight bearing physical activity within the limits of the underlying condition, and treating any associated endocrinopathies. With this paradigm shift in childhood osteoporosis evaluation and management, lack of DXA facilities to assess BMD at baseline and/or provide serial monitoring is not a major barrier for initiating IV bisphosphonate therapy in children in whom it is clinically indicated and would benefit from its use. DXA is useful, however, to monitor treatment response and optimal timing for treatment discontinuation in children with transient risk factors for osteoporosis. Overall, there is lack of awareness and paucity of guidelines on utilizing and adopting available resources to manage paediatric bone disorders optimally in lower-resource settings. We provide an evidence-based approach to the assessment and management of bone fragility disorders in children and adolescents, with appropriate considerations for lower resource settings including LMIC countries.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.911
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
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 head0.047
GPT teacher head0.374
Teacher spread0.327 · 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 designOther design
Domainnot available
GenreReview

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

Citations20
Published2023
Admission routes2
Has abstractyes

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