Multicenter study of compliance and drop administration in glaucoma.
Bibliographic record
Abstract
BACKGROUND: Poor compliance with medication is a major concern in the management of glaucoma. Improper administration technique can lead to contamination and inaccurate dosing. This study estimates the prevalence and predictors of noncompliance and improper administration technique among Canadian glaucoma patients. METHODS: Data were collected using a standardized questionnaire. Noncompliance was defined as missing at least 1 drop of medication per week and (or) the inability to accurately describe the medication regimen. Patients were asked to indicate the most common reason for missing medication. Study personnel assessed drop administration technique as patients were applying eye drops. Physicians provided information, including measures of disease stability, regarding the patient's glaucoma. Predictors were assessed using odds ratios from a logistic regression model. RESULTS: 500 patients from 10 centers across Canada participated in the study. Of these, 25.6% reported missing at least 1 drop of medication per week, and 4.2% were unable to accurately describe their medication regimen. The overall proportion of noncompliance was 27.9%. With regard to drop administration, 6.8% missed their eye and 28.8% contaminated the bottle tip; overall, 33.8% demonstrated improper technique. The most common reasons given for missing eye drops were "forgetfulness" and "being away from drops." Formal education limited to elementary school and treatment duration of <5 years increased patient-reported noncompliance. Factors associated with improper administration technique were age 60 years and older and formal education limited to elementary school. INTERPRETATION: Over 50% of the patients surveyed were either noncompliant or demonstrated improper administration technique. Glaucoma patients should be educated on the importance of compliance and instructed on proper drop administration.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| 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".