Correlation between protein C and Legg-Calve-Perthes disease: a Meta-analysis
Bibliographic record
Abstract
Objective To systematically evaluate the association between protein C and Legg-Calve-Perthes disease. Methods A literature research was performed through PubMed, Embase, Cochrane library, Web of Science, Chinese Biomedical Literature Database(CBM), China National Knowledge Infrastructure(CNKI) and Wanfang Database from inception to February 2016 on the association between protein C and Legg-Calve-Perthes disease.According to the Newcastle-Ottawa Scale(NOS) criteria, the quality of studies was evaluated and data were extracted.Meta-analysis was performed with Stata 11.0 software. Results A total of 14 articles were included.Twelve articles on protein C and Legg-Calve-Perthes disease in the study group and the control group were compared.The results of Meta-analysis showed that there was no significant difference in protein C levels between the study group and the control group[odds radio(OR)=1.41, 95% confidence interval(CI)(0.87, 2.28), P=0.147]; five articles on protein C and the white race of Legg-Calve-Perthes disease between the study group and the control group were compared, The results of Meta-analysis showed that there was no significant difference in protein C levels between the whiteskin patients′ group and the control group[OR=0.612, 95%CI(1.83, 7.29), P=0.612]; three articles on protein C and the yellow race of Legg-Calve-Perthes disease between the study group and the control group were compared, and the results of Meta-analysis showed that there was no significant difference in protein C levels between the yellow skin patients group and the control group[OR=0.59, 95%CI(0.05, 6.72), P=0.080]. Conclusion There is no significant correlation between protein C and Legg-Calve- Perthes disease. Key words: Legg-Calve-Perthes disease; Protein C; Meta-analysis; Child
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.048 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".