The correlation between C-reactive protein and normal tension glaucoma disease: A meta-analysis
Bibliographic record
Abstract
Background Normal tension glaucoma (NTG) is a common subtype of glaucoma that progresses silently and can lead to irreversible vision loss if left untreated. Emerging evidence suggests that inflammation and vascular dysfunction may play a role in its pathogenesis. C-reactive protein (CRP), a marker of systemic inflammation and atherosclerosis, has been widely studied as a prognostic indicator in various diseases. However, its potential association with NTG remains unclear. This meta-analysis aims to clarify the relationship between CRP levels in individuals with NTG compared to those without the condition. Methods We systematically searched PubMed, Embase, and Scopus for observational studies published up to 31 October 2023 investigating CRP levels in NTG patients and controls. Study quality and risk of bias were evaluated using the Newcastle–Ottawa Scale. Eligible studies reporting CRP levels were analyzed using standardized mean differences (SMDs) and 95% confidence intervals (CIs). Results A meta-analysis of ten case-control studies involving 766 patients revealed that CRP levels were significantly higher in the NTG group compared to controls (SMD: 0.731, 95% CI: 0.147–1.316 ; z = 2.454; P = 0.014). However, no significant difference in CRP levels was observed between the POAG group and controls (SMD = 0.093; 95% CI: −0.160–0.345; z = 0.719; P = 0.472). Conclusion Elevated circulating CRP levels were significantly associated with NTG, suggesting a potential systemic inflammatory contribution to its pathogenesis. Although CRP may serve as an adjunctive marker for identifying high-risk individuals, its clinical value remains provisional and requires confirmation in future prospective studies.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.049 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".