Evaluation of eyedrop administration by inexperienced patients after cataract surgery
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it