Management of Ocular Surface Disease in Glaucoma: A Survey of Canadian Glaucoma Specialists
Bibliographic record
Abstract
PRéCIS:: Ocular surface disease (OSD) in glaucoma is an area for improvement in the management of patients with glaucoma. This study explores the knowledge of glaucoma subspecialists toward OSD in glaucoma, then provides a suggested treatment algorithm. PURPOSE: To assess the attitudes, knowledge, and level of comfort of Canadian glaucoma specialists with respect to the assessment and management of OSD among patients with glaucoma. METHODS: Ophthalmologist members of the Canadian Glaucoma Society with fellowship training in glaucoma were contacted to participate in this cross-sectional survey study. Responses were recorded to statements regarding attitudes toward OSD in glaucoma, and assessment and management modalities. These were recorded primarily in the form of a Likert scale rated 1 to 7 from "strongly disagree" to "strongly agree." Descriptive statistics were generated, and mean and SD for responses on Likert scales. RESULTS: Thirty-six responses were included. All respondents agreed that comprehensive management of OSD could improve quality of life, 97% agreed it could lead to better glaucoma outcomes, whereas only 22% agreed it is presently being adequately managed in glaucoma practices. Respondents were asked to list all treatment modalities they felt knowledgeable about, ranging from 100% for optimizing topical glaucoma therapies to 31% for serum tears. Nearly all respondents (92%) agreed that a suggested algorithm for the treatment of OSD in glaucoma could improve their approach to management. CONCLUSION: OSD is a common comorbidity of glaucoma. Although respondents overwhelmingly agreed that comprehensive management of OSD may lead to improved quality of life and glaucoma-related outcomes, only a small percentage felt it was presently adequately managed. Increasing knowledge related to the assessment and management of OSD in glaucoma may in the future improve patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".