The association between obstructive sleep apneahypopnea syndrome and glaucoma: a Meta analysis
Bibliographic record
Abstract
Background Studies have showed an increasing prevalence of glaucoma in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS). However, this view remains controversial. Objective This Meta analysis was to assess whether there is an association between glaucoma and OSAHS. Methods A systematic search in PubMed database was carried out with the subject headingssleeping apneaandglaucoma. The literature type was limited to cases-controlled studies and prospective cohort studies with the publishing duration from January 1, 1982 to January 30, 2015 in English.The risk of glaucoma among OSAHS patients was analyzed, and Review Manager 5.2 was applicated for Meta analysis.The pooled odds ratio (OR) and 95% confidence interval (CI) was calculated to assess the strength of the association. Results Twelve independent retrospective cases-controlled studies were included in this review, including 11 592 subjects in the OSAHS group and 25 931 subjects in the control group.The study quality was scored 6-8 by Newcastle-Ottawa Scale which was acceptable.The random effects model was accepted because of the heterogeneity among the studies (χ2=34.20, P<0.05, I2=68%). The prevalence of glaucoma was higher in the OSAHS group than that in the control group (OR=1.87, 95%CI: 1.21-2.90, Z=2.82, P<0.05). The subgroup analysis of OSAHS showed that the OR (95% CI) of mild, moderate and severe OSAHS groups versus control group was 3.61 (0.56-23.43), 4.17 (0.47-36.91) and 6.95 (1.14-42.26), respectively.Sensitivity analysis confirmed that the OR value fluctuated in 1.74-2.16 and closed to 1.87.Funnel graphy exhibited a asymmetry appearance among the literatures, which suggested a possible publication bias. Conclusions OSAHS is one of the risk factors of glaucoma.The serious OSAHS is associated with an increased risk of glaucoma. Key words: Glaucoma/etiology; Sleep apnea, obstructive/complications; Comorbidity; Risk factors; Prevalence; Retrospective studies; Meta-analysis; Humans
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.057 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".