Factors Associated with Poor Eye Drop Administration Technique and the Role of Patient Education among Hong Kong Elderly Population
Bibliographic record
Abstract
Objectives. To identify the risk factors for poor eye drop application technique in treatment-naïve subjects and to assess if patient education can benefit these subjects. Methods. Chinese subjects above 60 years were recruited. Questionnaires, including Barthel index; Lawton’s instrumental activities of daily living (ADL); Fatigue, Resistance, Ambulation, Illnesses, and Loss of Weight (FRAIL) scale; and Montreal Cognitive Assessment (MoCA), were used to correlate with eye drop application technique (before and after patient education) using Spearman correlation analysis. A multiple linear regression was conducted to determine the predictors of successful administration technique and the improvement of technique after education. Results. The data from 26 subjects (mean age 72) were analyzed. Eye drop instillation technique score improved from 5.42 at baseline to 7.33 after clear instructions. FRAIL score was an independent predictor of baseline score ( p=0.003 ), as well as the improvement after patient education ( p=0.012 ). Age, sex, education level, visual acuity, Barthel index, MoCA, and ADL score were not correlated with eye drop instillation technique, before nor after patient education. Discussion. In patients with poor functional status as reflected by FRAIL score, eye drop application is prone to be ineffective. Education with step-by-step instructions could effectively improve the success of eye drop application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".