Meta-analysis of urinary C-terminal telopeptide of type II collagen as a biomarker in osteoarthritis diagnosis
Bibliographic record
Abstract
OBJECTIVE: This study aims to discover that the urinary C-terminal telopeptide of type II collagen (uCTX-II) levels differ between osteoarthritis (OA) patients and healthy individuals (controls). According to this difference, we may conclude that uCTX-II can be a biomarker for OA diagnosis. METHODS: We searched MEDLINE and EMBASE databases updated to 2014 to find literature on OA biomarkers. We retrieved the publications that met the required criterion. Literature quality was assessed according to the Newcastle-Ottawa Scale. Publication bias was assessed by Begg's test and Egger's test with the software STATA version 12.0. The weighted mean difference (WMD) was calculated, and the subgroup analysis was completed using STATA 12.0. RESULTS: Six publications were included in our analysis. The WMD for OA patients versus the controls was 83.05, which was within the 95% confidence interval. For subgroup analysis, the WMD of patients with severe OA was 119.92, whereas that of patients with mild OA was 28.07. CONCLUSIONS: uCTX-II levels were higher in OA patients than in controls, subgroup analysis revealed that the uCTX-II levels rised with the OA severity, the heterogeneity originated from different levels of OA severity, These results showed that uCTX-II would be a promising clinical biomarker in OA diagnosis.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".