Intraocular surgical site infection surveillance in Winnipeg, Manitoba, Canada
Bibliographic record
Abstract
Surveillance for surgical site infections (SSIs) after intraocular surgery may enable the detection of outbreaks and reveal opportunities for quality improvement. The reported incidence of SSIs after intraocular surgery varies widely, and there are no benchmark studies for SSI surveillance based on surveillance case definitions. We performed surveillance for SSIs after intraocular surgery at a regional ophthalmology surgical center in Winnipeg, Manitoba, Canada, from April 2014 to March 2023. Intraocular infection was defined according to the United States Centers for Disease Control and Prevention (CDC), National Healthcare Safety Network definition ( eye infection , other than conjunctivitis ). We defined an SSI as an intraocular infection occurring within 1 year after intraocular surgery, in contrast with the CDC definition of 30 or 90 days for other types of surgery. There were 96,322 intraocular operations performed during the 9-year surveillance period. The incidence of SSI for all types of intraocular surgery was 0.03% (3.0 infections per 10,000 operations), with substantially greater incidence after glaucoma valve implant surgery. We found that the incidence of SSI, identified using surveillance definitions, is lower than reported previously. This report provides benchmark data that may be useful for ophthalmological SSI surveillance initiatives at other institutions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".