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 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 | 0.022 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".