Role of excessive weight in intraocular pressure: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: This systematic review and meta-analysis aimed to analyse the effect of excess body weight on intraocular pressure (IOP) values. METHOD AND ANALYSIS: A literature search from PubMed, Medline and ScienceDirect Databases on 18 May 2023 was conducted by three reviewers, then filtered each study based on inclusion and exclusion criteria. For the quality assessment of included studies, the Newcastle-Ottawa Scale was adapted. Meta-analysis was performed using RevMan V.5.4 by entering the IOP values of each group to measure the mean difference. RESULTS: From 2656 studies, there were 9 studies that matched the criteria and then were included to perform a quantitative meta-analysis. The results showed a mean difference of 0.93 (95% CI: 0.67 to 1.18) of the excessive weight group against the normal weight group. This suggests that there is a significant relationship between excess body weight and increasing values of IOP. CONCLUSION: It can be concluded that excessive body weight tends to lead to higher IOP, which means that high IOP becomes a major risk factor for glaucoma.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".