Medical management of primary open-angle glaucoma: Best practices associated with enhanced patient compliance and persistency
Bibliographic record
Abstract
Primary open angle glaucoma is a chronic optic neuropathy often requiring lifelong treatment. Patient compliance, adherence and persistence with therapy play a vital role in improved outcomes by reducing morbidity and the economic consequences that are associated with disease progression. A literature review including searches of The Cochrane Library, MEDLINE, PubMed, conference proceedings, and bibliographies of identified articles reveals the enormous public health burden in various populations due to the impact of glaucoma associated visual impairment on the overall quality of life eg, fear of blindness, inability to work in certain occupations, driving restrictions, motor vehicle accidents, falls, and general health status. Providing specific definitions for the frequently misunderstood terms "compliance, persistence and adherence" with reference to medication use is central not only for monitoring patients' drug dosing histories and clinical outcomes but also for subsequent research. In this review article, a summary of the advantages/disadvantages including cost-effectiveness of various medical approaches to glaucoma treatment, techniques employed for measuring patient compliance and actual patient preferences for therapy are outlined. We conclude by identifying the key barriers to ongoing treatment and suggest some best practices to enhance compliance and persistence.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".