Eyedrop Instillation Techniques, Difficulties, and Currently Available Solutions: A Literature Review
Bibliographic record
Abstract
Purpose: To review current eyedrop instillation techniques, common difficulties faced by patients instilling eyedrops, available eyedrop assistive devices, and patient education regarding eyedrop instillation. Methods: PubMed, Embase, and Google Scholar were searched from conception until June 2022 for articles on eyedrop instillation difficulties, techniques, tools, and patient education. Results: Instillation involves pulling down the lower eyelids and placing drops on the corneal surface or conjunctival fornix, followed by closing of the eyelids for about 1 min. Examples of techniques include eyelid closure and nasolacrimal obstruction techniques. Patients encounter many difficulties when administering eyedrops, including but not limited to poor visibility, squeezing the dropper bottle, aiming the bottle, and accidentally blinking. However, devices are available that assist with aim and dropper compression-force reduction in eyedrop instillation. These can be particularly useful in patient demographics with diminished manual dexterity or the ability to generate force from their fingers. Furthermore, despite patient education in eyedrop instillation not being a common practice, it has been found that adequate patient education can lead to significant improvement in eyedrop instillation technique. Conclusions: While many factors are associated with poor eyedrop instillation technique, there are many solutions available including assistive devices and proper instillation education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".