Retinal Microvascular Response to Short-Term Exposure to Particulate Matters As an Indicator of Cardiovascular Effects in Work Environments
Bibliographic record
Abstract
Introduction: According to the literature, Central Retinal Arteriolar Equivalent (CRAE), Central Retinal Venular Equivalent (CRVE), and Artemio Venule Ratio (AVR) are three important markers in determining the changes of retinal artery. These markers are employed as an indicator of cardiovascular effects in workers. This study aimed to evaluate the changes in retinal micro vascular responses in workers exposed to short-term exposure to particulate matters caused by occupational processes using CRAE, CRVE, and AVR markers. Materials and Methods: In this study, 96 workers exposed to job-related pollution in a tile and ceramic company were investigated. Fundus photos of these people were analyzed via ARIA software using the related protocols. The data were investigated employing Kolmogorov–Smirnov and Mann-Whitney tests as well as the correlation and regression tests. Results: No significant difference was observed in the ocular parameters of smokers and non-smokers. Cardiovascular parameters had no significant correlation with CRAE and CRVE as well as breathable and inhalable dust; however, these parameters had a direct and significant correlation with AVR, PM10, and PM2.5 particles. Conclusion: The results did not show any changes in the retinal artery in workers who were exposed to job-related pollution. Moreover, no significant relationship was found between retinal artery changes and cardiovascular parameters. However, more large-scale studies are needed to clarify the relationship of particulate air pollutants with CRAE, CRVE, and AVR changes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".