Supplementary Material for: Retinal Nerve Fiber Layer Thickness Changes in the Pseudoexfoliation Syndrome: A Meta-Analysis of Case-Control Studies
Bibliographic record
Abstract
Purpose: To evaluate and compare changes in retinal nerve fiber layer (RNFL) thickness in patients with the pseudoexfoliation syndrome (PXS) and healthy controls. Methods: Case-control studies were selected through an electronic search of the Cochrane Controlled Trials Register, PubMed, and Embase. Results were reviewed to ensure that the included studies met prespecified inclusion/exclusion criteria, and the quality of each study was assessed using the Newcastle-Ottawa Scale. All included studies measured average and 4-quadrant (temporal, superior, nasal, and inferior) RNFL thickness using optical coherence tomography (OCT). For the continuous outcomes, we calculated the weighted mean difference (WMD) and 95% confidence intervals (CIs). Results: Eight case-control studies were included in this meta-analysis involving 225 eyes of PXS patients and 208 eyes of healthy controls in total. Statistical analysis revealed that the average RNFL thickness in PXS patients was significantly reduced compared to healthy controls (WMD = −6.91, 95% CI: −9.99 to −3.82, p < 0.0001). Additionally, differences in RNFL thickness in the superior quadrant (WMD = −10.68, 95% CI: −16.40 to −4.95, p = 0.0003), inferior quadrant (WMD = −8.20, 95% CI: −10.85 to −5.55, p < 0.00001), nasal quadrant (WMD = −3.05, 95% CI: −5.21 to −0.90, p = 0.005), and temporal quadrant (WMD = −6.39, 95% CI: −9.98 to −2.80, p = 0.0005) were all significant between the two groups. Conclusions: These results suggest that it is important to evaluate RNFL thickness and the optic nerve head through OCT in patients with PXS in order to determine early glaucomatous damage and start timely intervention prior to visual field loss.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.455 | 0.014 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".