Prevalence of scoliosis in children and adolescents: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The understanding of the prevalence and early predictive factors of scoliosis in children and adolescents is limited, which poses challenges to developing preventative strategies. This systematic review and meta-analysis aimed to clarify the prevalence and predictors of scoliosis among children and adolescents. Methods: We conducted a comprehensive search in PubMed, Cochrane, Embase, and Web of Science through October 2023. The quality of included studies was evaluated using the Joanna Briggs Institute scale or the Newcastle-Ottawa Scale. Subgroup analyses were performed to examine different types of scoliosis and specific demographic groups. Results: From 32 studies encompassing 55,635,351 children and adolescents, we identified 284,114 cases of scoliosis, resulting in a prevalence rate of 3.1% (95% CI: 1.5%-5.2%). This rate varied by gender, degrees of scoliosis severity, and between idiopathic vs. congenital forms. Notable predictors included gender, age, Body Mass Index (BMI), race, environmental factors, and lifestyle choices. Conclusion: Scoliosis is a significant condition affecting a minority of children and adolescents, particularly adolescent girls and individuals who are overweight. It is recommended that guardians and schools enhance educational efforts towards its prevention. Systematic Review Registration: https://www.crd.york.ac.uk/, Identifier CRD42023476498.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".