Bone Health in Autistic Children: Evidence from a Population-Representative Australian Cohort Study
Bibliographic record
Abstract
PURPOSE: Autistic children have been shown to have poorer bone health than their non-autistic peers, but previous evidence on this topic has been based on small clinical samples and is limited by how bone health has been measured. The association between autism and bone health may also reflect confounding by correlated genetic or environmental factors, but prior studies have not addressed this issue. METHODS: We address these issues using data from a population-representative cohort of Australian children with bone health measured by peripheral quantitative computed tomography (pQCT) for both children and parents. Data for 1,274 children (66 autistic, 1,208 non-autistic) aged 11-12 years (50% male) were drawn from the Child Health CheckPoint within the Longitudinal Study of Australian Children. pQCT measures at the tibial diaphysis (ankle) and metaphysis (shin) were recorded using an identical protocol for children and one attending parent. Child autism was parent reported. Regression analyses were used to compare differences between autistic and non-autistic children, and between parents of autistic children and parents of non-autistic children. RESULTS: Our findings indicate poorer bone health as assessed by tibial pQCT among autistic children compared to non-autistic children at both the metaphysis and diaphysis. No differences in pQCT measures were found between parents of autistic and non-autistic children, suggesting no evidence of confounding by shared genetic or environmental factors. CONCLUSION: These findings reinforce the need to support improved bone development among autistic children and suggest that differences in bone health are likely driven by behavioural factors that are potentially amenable to intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".