Vitreoretinal Surgery in the Post-Lockdown Era: Making the Case for Combined Phacovitrectomy
Bibliographic record
Abstract
Fares Antaki,1 Daniel Milad,2 Simon Javidi,1 Ali Dirani3 1Department of Ophthalmology, Centre Hospitalier de l’Université de Montréal (CHUM), Montreal, QC, Canada; 2Faculty of Medicine, Université Laval, Québec, QC, Canada; 3Department of Ophthalmology, Centre Universitaire d’Ophtalmologie, Hôpital du Saint-Sacrement, CHU de Québec - Université Laval, Québec, QC, CanadaCorrespondence: Ali DiraniDepartment of Ophthalmology, Centre Universitaire d’Ophtalmologie, Hôpital du Saint-Sacrement, CHU de Québec - Université Laval, Québec, CanadaEmail drdirani@gmail.comAbstract: The coronavirus disease (COVID-19) pandemic has significantly limited the capacity of healthcare systems to provide elective services like cataract surgery. Cataract formation is a frequent complication after pars plana vitrectomy. In this paper, we review the pros and cons of combined phacovitrectomy as opposed to sequential surgery in the post-pandemic era. In particular, we discuss the patient-level visual benefits and societal economic advantages of this procedure.Keywords: COVID-19, phacovitrectomy, vitreoretinal surgery, retinal detachment, macular hole, epiretinal membrane, cataract
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".