Clinical Insights Concerning Rickets in Association With Neurodevelopmental Disorders: A Systematic Review Focused on Autism
Bibliographic record
Abstract
This systematic review examines the intersection of rickets and autism spectrum disorder (ASD), highlighting clinical insights from 10 studies involving 13 patients. Rickets, a pediatric bone disorder resulting from vitamin D, calcium, and/or phosphate deficiencies, often manifests through skeletal deformities, muscle weakness, and bone pain. ASD is a neurodevelopmental condition characterized by deficits in social communication and repetitive behaviors. The coexistence of these conditions, although uncommon, is notable due to overlapping factors such as selective eating habits and sensory sensitivities in children with ASD, which may exacerbate nutritional deficiencies. A comprehensive search across PubMed, Google Scholar, and Web of Science identified 65 relevant articles meeting the initial inclusion criteria, 10 of which (reporting on 13 patients) met the final review criteria for inclusion. All patients exhibited food selectivity, with most excluding dairy products and favoring potato-based foods. Musculoskeletal symptoms were predominant, with genu valgum, wrist widening, and metaphysis fraying being common findings. Seizures were the second most frequent reason for hospital admission, emphasizing the importance of monitoring neurological health in these patients. Calcium supplementation, primarily with calcium carbonate or calcium gluconate, was used in all cases, and the majority of patients experienced normalization of biochemical markers, including serum calcium levels. This review underscores the need for interdisciplinary care, focusing on nutritional and behavioral interventions to manage both rickets and ASD effectively. Future research should aim to explore larger and more diverse populations to better understand the clinical interplay between these conditions and inform more comprehensive treatment strategies.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".