38 High prevalence of fractures and pain in children with medical complexity cohort
Bibliographic record
Abstract
Abstract Background Children with Medical Complexity (CMC) are at high risk of sustaining fragility fractures due to low bone mineral density (BMD). These patients are exposed to multiple factors like limited mobility, feeding difficulties and medication that impair bone density gain. Pain or discomfort are currently reported by their caregiver, due to many conditions. Among them, fragility fractures are a frequent cause of acute and chronic pain, but are often underdiagnosed and could be prevented. Children with cerebral palsy have an annual fracture incidence double that of typically developing children. An individual’s risk for osteoporosis can be monitored by measuring BMD and identifying other clinical risk factors (such as ancestry, age, sex, pubertal stage, medication). Objectives We collected datas on a cohort of CMC recruited for a project in progress about the association between a polygenic risk score and variance of BMD. By presenting these datas, our objective is to prove high prevalence of fragility fractures and subsequently pain in a high-risk population, where robust data on bone health are lacking Design/Methods We conduct a single-center prospective cohort study in which we recruit children aged 1 to 18 years diagnosed with either cerebral palsy or Duchenne muscular dystrophy plus medical complexity. Recruitment is conducted by a research nurse, with physical examination performed by paediatricians. Medical history includes fracture occurrence and pain. Participants undergo spinal X-rays and DXA scans to evaluate vertebral fractures and BMD. All study visits align with participants’ regular hospital appointments. Results Sixty-nine participants have been successfully recruited into our study. Median age is 7.5 years [1-17¬]. The results reveal low BMD z-scores in spine (median -1.8 [-7.1 – 1.6]) and severely low hip BMD z-scores (median -3.5 [-5.2 – 2.3]), a high prevalence of fracture (29%) and reported pain (60%). Fractures occur more often in long bones, while pain is usually reported in the back. 41% have scoliosis. Many of them are exposed to anti-epileptic therapy (50%) or ketogenic diet (11.5%). Some are already treated for OP (33% receive bisphosphonates). Conclusion Our data prove a high prevalence of fractures and reported pain in this high-risk population, which could be prevented by screening them for fragility fractures and low-BMD. Developing targeted prevention strategies for bone health in children requires more robust data in this topic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".