Disinfection of Multi-Use Ocular Equipment for Ophthalmological Procedures: A Review of Clinical Effectiveness, Cost-Effectiveness, and Guidelines [Internet]
Bibliographic record
Abstract
In ophthalmology, there are certain equipment that are used and reused across different patients within a medical practice that resulting in indirect contact between multiple patients. This could pose a risk of cross infection between patients, especially with viruses and bacteria. One example of such equipment would be the tonometer, a device to measure the intraocular pressure in patients to determine risk of glaucoma. The tonometer tip is in direct contact with the patient’s eye and studies have demonstrated the transmission of hepatitis B virus, hepatitis C virus, human immunodeficiency virus (HIV), and Creutzfeldt-Jakob disease can occur between patients. Therefore, equipment cleanliness is critical.The Canadian Optometrists Association has a general infection control guideline; however, in general, among available guidelines, there is little consistency and guidance in what the best approach would be to reduce transmission of diseases between patients. In vitro studies compared various sterilization techniques to determine whether or not viral particles are removed from the ophthalmic equipment but it is important to evaluate the impact of these cleanliness techniques on clinically relevant outcomes, such as infection transmission. Various guidelines and recommendations exist but it is unclear if there is any association between these techniques and disease transmission between patients.The objective of this review is to evaluate the comparative clinical and cost-effectiveness of various disinfection techniques and/or procedures for multi-use ocular equipment in ophthalmology patients, as well as the guidelines for its use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".