The impact of chronic use of prostaglandin analogues on the biomechanical properties of the cornea in patients with primary open-angle glaucoma
Bibliographic record
Abstract
Aims To determine the influence of prostaglandin analogues (PGAs) on corneal biomechanical properties in patients undergoing chronic treatment for primary open-angle glaucoma (POAG). Methods Prospective, interventional case–control study. 70 eyes from 35 patients with POAG on chronic PGA therapy were recruited. One eye per patient underwent PGA cessation for 6 weeks while the contralateral eye continued to receive the treatment. Corneal hysteresis (CH), corneal resistance factor (CRF), corneal-compensated intraocular pressure (IOP) (IOPcc), central corneal thickness (CCT) and Goldmann tonometry (Haag-Streit AG, Koeniz, Switzerland) IOP (IOPGAT) were measured at baseline (visit 1), 6 weeks after PGA cessation (visit 2) and 6 weeks after PGAs reinitiation (visit 3) and were analysed using a linear mixed-effect model. The discrepancy between IOPcc and IOPGAT was defined as IOP bias (IOPcc—IOPGAT). Results Baseline characteristics were comparable between the two groups. In the study eyes, significant increases (p<0.0001) were detected at visit 2 in CH (9.0±1.8 vs 10.3±1.7 mm Hg), CRF (10.5±2.1 vs 11.7±2.1 mm Hg), CCT (541.8±43.2 vs 551.9±41.9 μm) and IOPGAT (15.4±3.0 vs 18.4±3.8 mm Hg). IOP bias in this group was significantly lowered at visit 2 (p<0.0001). These effects were reversed at visit 3. The control eyes did not demonstrate any significant changes over the study period. Conclusion Topical PGAs induce reversible reduction in CH, CRF and CCT in patients with POAG. These changes contribute to underestimation of the IOP measured by Goldmann applanation tonometry and warrant caution when assessing response to treatment. Trial registration number NCT02388360, Results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".