Evidence on risk factors for knee osteoarthritis in middle-older aged: a systematic review and meta analysis
Bibliographic record
Abstract
Abstract Purpose This review was made to identify the risk factors for knee osteoarthritis (KOA) in middle-older aged (≥ 40 years), and to provide the newest evidence for the prevention of KOA. Method Cohort study and case–control study of the risk factors of KOA was included from Pubmed, Web of Science, Ovid Technologies, China National Knowledge Infrastructure (CNKI), Chinese Science and Technology Periodical Database (VIP), Wanfang Database, SinoMed from their inceptions to July 2023. Two authors independently screened the literature and extracted data. Assessment of quality was implemented according to Agency for Healthcare Research and Quality (AHRQ) and Newcastle–Ottawa Quality Assessment Scale. Meta-analysis was performed using RevMan 5.3 software. Results 3597 papers were identified from the seven databases and 29 papers containing 60,354 participants were included in this review. Meta-analysis was performed for 14 risk factors, and 7 of these were statistical significance (P < 0.05). The risk factors which were analyzed in this review included trauma history in knee (1.37 [95% CI 1.03–1.82], P = 0.030), body mass index (BMI) ≥ 24 kg/m2 (1.30 [95% CI 1.09–1.56], P = 0.004), gender (female) (1.04 [95% CI 1.00–1.09], P = 0.030), age ≥ 40 (1.02 [95% CI 1.01–1.03], P = 0.007), more exercise (0.75 [95% CI 0.62–0.91], P = 0.003), a high school education background (0.49 [95% CI 0.30–0.79], P = 0.003) and an university education background (0.22 [95% CI 0.06–0.86], P = 0.030). Conclusion The risk factors analyzed in this review included trauma history in knee, overweight or obesity, gender (female), age ≥ 40 and the protective factors included more exercise and a high school or an university education background.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.027 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".