Diabetes Mellitus and Keratoconus: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Although previous studies have assessed the relationship between diabetes and keratoconus, the findings were controversial and warranted further clarifications. The objective of this study was to investigate the association between diabetes and keratoconus by conducting a systematic review and meta-analysis. METHODS: A comprehensive literature search was performed to identify eligible studies reporting the association of diabetes with keratoconus from their inception to April 2021 through PubMed, Embase, and Web of Science. The quality of included studies was assessed using the Newcastle-Ottawa scale. Combined odds ratios (ORs) and 95% confidence intervals were calculated using a random-effects model. RESULTS: In all, 8 case-control studies and 3 cohort studies reporting the association between diabetes and keratoconus were included in the meta-analysis. Diabetes was not associated with keratoconus in the overall analysis (combined OR = 0.85, 95% confidence interval: 0.66-1.10). The associations were found to be nonsignificant in subgroup analysis when stratified by study quality, design, source, types, and population. No publication bias was detected from either the Egger test (P = 0.46) or Begg test (P = 0.16). Sensitivity analysis revealed that differences between groups were not statistically significant. CONCLUSIONS: This meta-analysis indicates that current literature does not support a significant association between diabetes and keratoconus. Further studies with more definite control for confounders and well-designed cohorts or interventions are warranted.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".