Intermediate impacts of COVID-19 lockdowns on surgical glaucoma cases in Quebec, Canada: insights from a tertiary eye center
Bibliographic record
Abstract
AIM: To assess the effect of the coronavirus disease 2019 (COVID-19) pandemic on the wait times and severity of surgical glaucoma cases in a single tertiary referral center in Quebec, Canada. METHODS: Preoperative severity data included mean visual field (VF) deficit, intraocular pressure (IOP), the number of topical glaucoma medication classes, and preoperative best corrected visual acuity (BCVA). The times from referral to procedure (referral time) and from listing date to procedure (waitlisting time) were calculated. RESULTS: This retrospective cohort study involved 181 eyes of patients undergoing glaucoma surgery from March 1 to June 30, 2019 (pre-pandemic period), and 201 eyes in the same timeframe in 2021 (pandemic period) at Saint-Sacrement Hospital in Quebec City. There was no significant difference in the severity data of surgical glaucoma across both periods (VF deficit: P=0.48; IOP: P=0.14; BCVA: P=0.24; topical medication classes: P=0.27). The number of patients referred with oral glaucoma medication increased slightly from 45 to 70 in 2019 and 2021 respectively (P=0.08). Delay data were also comparable. Mean referral time was 122±120d in 2019 versus 144±136d in 2021 (P=0.09), whereas waitlisting time before the pandemic was 43±44.5 versus 39±41.8d in 2021 (P=0.13). CONCLUSION: Despite North America’s strictest pandemic restrictions, limited negative impact is observed on waitlisting delays and the severity of glaucoma cases presenting at our center. A larger subset of patients is treated with oral medications indicating a possible increase in advanced glaucoma.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".