META-ANALYSIS ON THE PREVALENCE OF CORNEAL ULCER IN BRAZIL (2021-2024)
Bibliographic record
Abstract
Objective: This meta-analysis aimed to synthesize the prevalence of corneal ulcer in Brazil between 2021 and 2024, addressing regional variability, methodological heterogeneity, and data scarcity. Methods: Following PRISMA 2020 and MOOSE guidelines, a comprehensive search across PubMed, Scopus, Embase, Web of Science, and Google Scholar was conducted for observational studies reporting prevalence data on corneal ulcer in Brazilian populations. Two reviewers extracted data independently and assessed methodological quality using the Newcastle Ottawa Scale. Random-effects models were used to pool prevalence estimates, and heterogeneity was assessed via Cochrans Q and Istatistics. Conclusions: Corneal ulcer remains a significant ocular public health issue in Brazil. The findings underscore the need for standardized diagnostic protocols and continuous epidemiological surveillance to reduce blindness burden.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.051 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".