Strategies for the Management of Ocular Surface Disease in Glaucoma
Bibliographic record
Abstract
Ocular surface disease (OSD) is a common ophthalmological concern, with a prevalence in the Canadian population estimated at 25%. Amongst glaucoma patients, up to 60% report symptoms of OSD and up to 78% have clinical signs. Surface symptoms significantly reduce glaucoma-related quality-of-life (QOL), and there is emerging evidence to suggest that treatment of OSD may in fact improve intraocular-pressure (IOP) control and contribute to disease stabilization. The treatment of OSD in glaucoma has been receiving increasing attention, however specific recommendations remain sparse. Ocular surface disease is known to represent a complex milieu of genetic predisposition, adnexal and periorbital status, environmental factors, systemic diseases, and medications (topical and systemic), amongst other predisposing factors. Effective management of this condition therefore demands that treatment be targeted to the specific clinical context. A 2020 study of Canadian glaucoma specialists found that, although 97% identified optimization of ocular surface disease as important for improving patient QOL, only 22.2% felt this was currently being managed adequately in the subspecialty clinic setting. Moreover, although all participants felt comfortable modifying topical hypotensive regimens to improve surface disease, only 61.1% were confident identifying patients who would benefit from topical steroids, and just 30.5% felt knowledgeable regarding the use and dispensation of autologous serum tears, which are increasingly deployed for treatment-resistant OSD in dry-eye practices. There is, therefore, an unmet need for clarity in the treatment algorithm to optimize OSD in glaucoma patients. Here, we discuss the therapeutic approach to these patients and present a suggested algorithm to guide management.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.037 | 0.011 |
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".