Hypermobility and musculoskeletal pain in children: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To examine the evidence of an association between hypermobility and musculoskeletal pain in children. METHODS: A systematic review of the literature was performed using the databases PubMed, EMBASE, NHS Evidence, and Medline. Inclusion criteria were observational studies investigating hypermobility and musculoskeletal pain in children. Exclusion criteria were studies conducted on specialist groups (i.e. dancers) or hospital referrals. Pooled odds ratios (ORs) were calculated using random effects models and heterogeneity was tested using χ(2)-tests. Study quality was assessed using the Newcastle-Ottawa Scale for case-control studies. RESULTS: Of the 80 studies identified, 15 met the inclusion criteria and were included in the review. Of these, 13 were included in the statistical analyses. Analysing the data showed that the heterogeneity was too high to allow for interpretation of the meta-analysis (I(2) = 72%). Heterogeneity was much lower when the studies were divided into European (I(2) = 8%) and Afro-Asian subgroups (I(2) = 65%). Sensitivity analysis based on data from studies reporting from European and Afro-Asian regions showed no association in the European studies [OR 1.00, 95% confidence interval (CI) 0.79-1.26] but a marked relationship between hypermobility and joint pain in the Afro-Asian group (OR 2.01, 95% CI 1.45-2.77). Meta-regression showed a highly significant difference between subgroups in both meta-analyses (p < 0.001). CONCLUSION: There seems to be no association between hypermobility and joint pain in Europeans. There does seem to be an association in Afro-Asians; however, there was a high heterogeneity. It is unclear whether this is due to differences in ethnicity, nourishment, climate or study design.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".