When to declare an endophthalmitis outbreak: Poisson model of postcataract surgery endophthalmitis outbreak projections
Bibliographic record
Abstract
PURPOSE: To create an accessible, online tool that determines the likelihood of an endophthalmitis outbreak. SETTING: Surgical centers that perform cataract surgeries. DESIGN: Mathematical modelling study. METHODS: The global risk of acute postoperative endophthalmitis (POE), defined as occurring within 6 weeks after standalone cataract surgery, was determined from a systematic review. An online tool was then created using the Poisson model to determine the likelihood of a POE outbreak. The model used the number of cataract surgeries performed, time, and the number of POE cases observed. Significance was defined as P < .05, P < .01, or P < .001. RESULTS: From the 25 included studies, the mean standard risk of POE after standalone cataract surgery was 0.0692% (range: 0.0189% to 0.102%). The model demonstrated that for a center that performs 1000 cataract surgeries in a month, the probability of at least 1 POE case occurring in a month is not statistically significant ( P = .461, P > .05), and therefore, an outbreak is unlikely. By contrast, the probability of at least 3 POE cases occurring in a month at this center is statistically significant ( P < .05), and thus, has moderate evidence for an outbreak with 95% confidence. CONCLUSIONS: The Poisson model represents an evidence-based way to determine whether the number of POE cases is within expectations for the number of cataract operations performed over a period. This online tool provides ophthalmologists and public health agencies with helpful data to guide POE outbreak declaration decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".