Survey of Complementary and Alternative Medicine Use in Glaucoma Patients
Bibliographic record
Abstract
PURPOSE: To determine the prevalence, types, and associated factors of complementary and alternative medicine (CAM) use in glaucoma patients. PATIENTS AND METHODS: Prospective, multicenter, cross-sectional survey. A total of 1516 consecutive patients attending 2 tertiary glaucoma clinics were surveyed on CAM use. Information gathered on standardized data collection sheets included demographic variables, ophthalmic history, glaucoma treatment history, and details of CAM use. RESULTS: The response rate was 92.5%. A total of 166 patients (10.9%) reported current use of CAM therapy specifically for glaucoma whereas 41 patients (2.7%) reported past use of CAM. Of the patients who reported CAM use, 62.5% had not disclosed the use of CAM to their ophthalmologist and 40.5% believed that the treatments were helping their glaucoma. The most commonly used types of CAM were herbal medications (34.5%), dietary modifications (22.7%), and vitamin/mineral supplements (18.8%). Of the 207 patients who reported current or past CAM use for their glaucoma, 3 (1.4%) indicated that they used conventional glaucoma treatments < prescribed because of their CAM use. CONCLUSIONS: Approximately 1 in 9 glaucoma patients use CAM for their disease. Many of these patients do not disclose the use of CAM to their ophthalmologist, but the vast majority report that they still take conventional glaucoma medications as prescribed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".