Intravitreal Injections during the COVID-19 Outbreak in Northern Italy: An Innovative Approach for a High Quality and Safe Treatment
Bibliographic record
Abstract
BACKGROUND: Intravitreal injection (IVI) is a standard procedure performed in ophthalmology to treat several conditions, and is performed in different settings across countries. The Italian guidelines recommend this intervention is performed in an operating room to minimize the risk of infections, while in other countries, including Canada, USA and the UK, IVIs are performed in the ophthalmologist's office. The 2020 COVID-19 outbreak caused a dramatic modification in outpatient care. Consequently, non-urgent surgical activities, like IVIs, were subjected to a drastic reduction. METHODS: We conducted observational study which investigated the outcomes of IVIs performed in an ophthalmologist's office using a mobile laminar flow unit, the Operio mobile (Toul Meditech, Operio®) versus an operating room setting. RESULTS: Use of the Operio mobile allowed the safety performance of 3838 IVIs during COVID-19 and significantly reduced the waiting time of the first visit. This results in a faster intervention without affecting the technical IVI procedure that remained unchanged comparing the two settings. Specifically, we observed a 26% reduction in operation costs for each IVI performed in the office, which can be translated to a higher impact when considering the total number of IVIs performed over one year. CONCLUSION: The use of the Operio mobile in an ophthalmologist's office provides flexibility to perform IVIs, assuring patient safety, reducing healthcare personnel employment times, and the waiting lists for the patients, increasing the number of surgeries and improving the cost-effectiveness of the procedure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".