Eye drop administration in patients attending and not attending a glaucoma education center
Bibliographic record
Abstract
BACKGROUND: To assess the technique of glaucoma eye drop instillation in patients who have and have not attended glaucoma education sessions. To compare this with their subjective perception of eye drop use and identify factors associated with improved performance. PATIENTS AND METHODS: An observational study of 55 participants who instill their topical glaucoma medication for more than 1 year. Twenty-five patients attended (A) glaucoma teaching sessions >1 year before the study and were compared to thirty patients who never attended (NA). Patients completed a self-reporting questionnaire. They instilled their eye drop, and the technique was video-recorded digitally and later graded by two masked investigators. The results were analyzed using Fisher's exact test and Chi-square test. Predictors were assessed using logistic regression models. RESULTS: There was no significant difference in overall performance scores between the two groups. Good technique was observed in 16% of (A) group versus 23% (NA) group, (P = 0.498). There was a mismatch between patient's subjective and actual performance. Female gender and higher educational level were found to be predictors of good performance of drop instillation on univariable logistic regression analysis. CONCLUSION: Glaucoma patients are challenged with eye drop instillation despite receiving education on drop administration. There is a discrepancy between patient's perceptions and observed technique of 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".