Association between ICAM-1 level and diabetic retinopathy: a review and meta-analysis
Bibliographic record
Abstract
Elevated levels of proinflammatory markers are evident in patients with diabetic retinopathy (DR) and are associated with disease progression and prognosis. Intercellular adhesion molecule-1 (ICAM-1) is involved in inflammation and acts as a local intensifying signal in the pathological processes associated with chronic eye inflammation. The aim of this systematic review and meta-analysis was to investigate the relationship between ICAM-1 level and DR. Online electronic databases were searched to retrieve all relevant articles published before December 2017. The standard mean difference (SMD) and their 95% CI were included and then pooled with a random effects model. Subgroup analysis and metaregression analysis were applied to explore the sources of heterogeneity, and publication bias was calculated to assess the quality of pooled studies. A total of 11 articles, containing 428 patients with DR and 789 healthy controls, were included in this meta-analysis. The results indicated a significant increase in ICAM-1 level in the DR group compared with the control group (SMD: 1.20, 95%CI 0.83 to 1.57, p<0.001). Subgroup analyses and metaregression analysis indicated that publication year, region, study method, diabetes mellitus type, Newcastle-Ottawa Scale and sample size were not the potential sources of heterogeneity. The results of this current meta-analysis indicated that the increased level of ICAM-1 generally exists in the patients with DR and it may associated with the severity of DR. However, large-scale and high-quality studies are required to confirm this finding in the future.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".