Evaluation of eyedrop administration by inexperienced patients after cataract surgery
Bibliographic record
Abstract
PURPOSE: To evaluate efficacy and safety of eyedrop administration after cataract surgery and to identify predictors of better technique in patients without previous eyedrop experience. SETTING: Department of Ophthalmology, McGill University, Jewish General Hospital, Montreal, Quebec, Canada. DESIGN: Prospective cross-sectional study. METHODS: Eyedrop-naïve postoperative cataract patients were consecutively recruited the day after cataract surgery. Data were collected using a standardized self-reporting questionnaire and a chart review and by videotaping patients administering the drops in the operated eye. Two independent observers objectively evaluated the instillation technique. Predictors were assessed using odds ratios (ORs) from a logistic regression model. RESULTS: The study enrolled 54 patients. Subjectively, 17 patients (31%) reported difficulty instilling the eyedrops. Sixty-nine percent reported always washing their hands before using the drops, 42% believed that they never missed their eye when instilling drops, and 58.3% believed they never touched their eye with the bottle tip. Objectively, 50 patients (92.6%) showed an improper administration technique, including missing the eye (31.5%), instilling an incorrect amount of drops (64.0%), contaminating the bottle tip (57.4%), or failing to wash hands before drop instillation (78.0%). A better performance score was significantly associated with having received instructions on how to use drops (OR, 11.99; P=.011). CONCLUSIONS: Postoperative cataract patients inexperienced with eyedrop use showed a poor instillation technique by failing to wash hands, contaminating bottle tips, missing the eye, and using an incorrect amount of drops. There was a large discrepancy between the patients' perceptions and the observed technique of drop administration. FINANCIAL DISCLOSURE: No author has a financial or proprietary interest in any material or method mentioned.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".