Osteoclast indices in osteogenesis imperfecta: systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Osteogenesis imperfecta (OI) is a rare bone fragility disorder caused by mutations in genes encoding collagen type I or that affect its processing. Alterations in osteoclasts were suggested to contribute to OI pathophysiology. We aimed to systematically identify studies reporting measures of osteoclast formation and function in patients and mouse models of OI, to quantify OI-induced changes. The systematic search of Medline, Ovid, and Web of Science identified 798 unique studies. After screening, we included 23 studies for meta-analysis, reporting osteoclast parameters in 310 patients with OI of 9 different types and 16 studies reporting osteoclast parameters in 406 animals of 11 different OI mouse models. The standardized mean difference with 95% confidence interval (CI) was used as the effect size, and random-effects meta-analysis was performed. In patients with OI, collagen degradation markers were significantly higher compared with age-matched controls, with an effect size of 1.23 (CI: 0.36, 2.10]. Collagen degradation markers were the most elevated in the 3- to 7-year-old age group and in patients with more severe forms of OI. Bone histomorphometry demonstrated the trends for higher osteoclast numbers (1.16; CI: −0.22, 2.55) and osteoclast surface (0.43; CI: −0.63, 1.49), and significantly higher eroded surface (3.24; CI: 0.51, 5.96) compared with age-matched controls. In OI mice, meta-analysis demonstrated significant increases in collagen degradation markers (1.59; CI: 1.07, 2.11), in osteoclast numbers (0.94; CI: 0.50, 1.39), osteoclast surface (0.73; CI: 0.22, 1.23), and eroded surface (1.31; CI: 0.54, 2.08). The largest differences were in OI mice with the mutations in Col1a1 and Col1a2 genes. There were no differences between males and females in clinical or animal studies. Quantitative estimates of changes in osteoclast indices and their variance for patients with OI are important for planning future studies. We confirmed that similar changes are observed in mice with OI, supporting their translational utility.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".