Supplementary Material for: The Effect of Prostaglandin Analogues on Central Corneal Thickness of Patients with Glaucoma or Ocular Hypertension: a systematic review
Bibliographic record
Abstract
Purpose: A meta-analysis of observational studies was conducted to evaluate the effect of prostaglandin analogues (PGAs) on central cornea thickness (CCT) of patients with glaucoma and ocular hypertension (OHT). Methods: A literature search was performed through Pubmed, Embase, Cochrane Library, the System for Information on Grey Literature in Europe (Open Grey), ClinicalTrials.gov, and reference lists of retrieved studies. Observational studies were included in our meta-analysis. The final CCT of patients and 95% confidence interval (CI) from each study were extracted. Study quality was assessed using The Newcastle-Ottawa Scale (NOS) and the Agency for Healthcare Research and Quality (AHRQ). A fixed-effects model was used to calculate the weighted mean difference (WMD) and 95% confidence interval (CI). Subgroup analyses based on several stratified factors were also performed. Results: Five cohort studies, five case-control, three cross-sectional studies involving 2722 subjects were included. The pooled effect of all thirteen studies showed PGAs can reduce CCT of patients with glaucoma or OHT slightly but significantly (WMD, -9.37; 95% CI [-12.18, -6.57], P =0.00; I2 = 45.5%). And significant results were observed in all specific study design ( WMD, -5.17; 95% CI [-9.52, -0.82] for cohort study; WMD, -15.31; 95% CI [-22.66, -7.97] for case–control study; WMD, -8.65; 95% CI [-17.30, -0.01] for cross-sectional study). Also, subgroup analysis of exposure time showed the effect of PGAs was more obvious in the first two years (WMD, -5.81; 95% CI [-9.49, -2.14] for 1 year; WMD, -13.02; 95% CI [-20.03, -6.01] for 2 years). Conclusions: The pooled effects from current literature suggest that PGAs use could reduce CCT of patients with glaucoma or OHT slightly but significantly, and this effect is more pronounced in the first two years. This reminds us that we need to pay attention to the changes in CCT during the first two years of PGA use in case we misestimate intraocular pressure (IOP) and the efficacy of the PGA.
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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 | 0.005 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.519 | 0.020 |
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".