Association of Age and Sex at Onset With Glenohumeral Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: The aim of the present systematic review is to synthesize existing evidence (qualitative and quantitative) regarding age- and sex-specific differences with glenohumeral osteoarthritis. DESIGN: The electronic databases PubMed, MEDLINE, and Web of Science were searched up to March 15, 2023. Articles reporting on the association of risk factors (age and sex) with glenohumeral osteoarthritis were considered. We used Newcastle-Ottawa Scale to assess study quality. Meta-analysis was conducted to quantitatively summarize the association of age and sex with glenohumeral osteoarthritis. RESULTS: A total of 24 articles were retrieved for full-text review. Of 24 articles, 8 reporting age-specific and 5 articles reporting sex-specific associations with glenohumeral osteoarthritis were included. The odds ratio for the age (odds ratio = 3.18; 95% confidence interval = 1.10-15.92) and female sex (odds ratio = 1.78; 95% confidence interval = 0.95-3.42) were increased and observed statistically significant. CONCLUSIONS: The present systematic review and meta-analysis suggests the role of increasing age as one of the significant contributors to glenohumeral osteoarthritis. However, association of female sex with glenohumeral osteoarthritis is least convincing. Future studies are required to understand the molecular mechanisms behind the contributory role of increasing age and female sex in the establishment of glenohumeral osteoarthritis.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.003 | 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".