PRISMA_2020_abstract_checklist.pdf
Bibliographic record
Abstract
Previous studies have linked genetics to knee osteoarthritis. Angiotensin-converting enzyme (ACE) gene I/D polymorphism may cause OA. However, evidence remains inconsistent. This study examines knee OA risk and ACE gene I/D polymorphism. We explored Europe PMC, Medline, Scopus, and Cochrane Library using keywords. Three assessment bias factors were assessed using the Newcastle-Ottawa Scale (NOS). Criteria for inclusion: (1) Split the study population into knee OA patients and healthy controls; (2) Analysed the ACE gene I/D polymorphism; (3) Case-control or cross-sectional surveys. Studies with non-knee OA, incomplete data, and no full-text were excluded. The odds ratio (OR) and 95% confidence intervals (95% CI) were calculated using random-effect models. A total of 6 case-control studies consist of 1,226 patients with knee OA and 1,145 healthy subjects as controls were included. Our pooled analysis revealed that a significant association between ACE gene I/D polymorphism and risk of knee OA was only seen in the dominant (DD + ID vs. II) [OR 1.69 (95% CI 1.14 – 2.50), <i>p</i> = 0.009, <i>I</i><sup>2</sup> = 72%], and ID vs. II [OR 1.37 (95% CI 1.01 – 1.86), <i>p</i> = 0.04, <i>I</i><sup>2</sup> = 43%] genotype models. Other genotype models, including recessive (DD vs. ID + II), alleles (D vs. I), DD vs. ID, and DD vs. II models did not show a significant association with knee OA risk. Further regression analysis revealed that ethnicity and sexsex may influence those relationships in several genotype models. Dominant and ID vs. II ACE gene I/D polymorphism models increased knee OA risk significantly. More research with larger samples and different ethnic groups is needed to confirm our findings. After ethnicity subgroup analysis, some genetic models in our study showed significant heterogeneities, and most studies are from Asian countries with Asian populations, with little evidence on Arabs
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.994 | 0.995 |
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; both teacher heads agree on what is shown here.
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".