In vitro efficacy of topical ophthalmic antiseptics against SARS-CoV-2
Bibliographic record
Abstract
Shedding of SARS-CoV-2 in tears of patients with COVID-19 has been reported,1 2 which could serve as a source of infection for healthy individuals, including healthcare providers. The current standard antiseptic solutions used in ophthalmology in the setting of inoffice procedures and operating rooms include povidone-iodine (PVI) 5% and chlorhexidine gluconate (CHX) 0.1% or 0.05%, which are at concentrations that are lower than those used in other surgical specialties. Although laboratory and clinical studies to date have aimed to evaluate the virucidal benefits of routine PVI use for ophthalmic surgeries,3 currently there are no established guidelines regarding the optimal contact time and efficacy of varying dilutions as well as comparisons with other formulations such as CHX. Rigorous evaluation of the efficacy of virucidal agents for disinfecting ocular surface of potentially infected patients with SARS-CoV-2 is critical in mitigating the risk of transmission. In the current study, we evaluated the virucidal efficacy and contact times for commonly used ophthalmic concentrations of PVI and CHX against SARS-CoV-2 using Vero E6 cells as indicator cell lines for residual viable virus based on previously established methodologies (online supplemental appendix).4–6 PVI (5% weight per volume, w/v) and CHX (0.05% and 0.1% w/v) were tested at full strength. Fifty microlitres of ophthalmic formulations …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".