Secure Surveillance of Antimicrobial Resistant Organism Colonization or Infection in Ontario Long Term Care Homes
Bibliographic record
Abstract
BACKGROUND: There is stigma attached to the identification of residents carrying antimicrobial resistant organisms (ARO) in long term care homes, yet there is a need to collect data about their prevalence for public health surveillance and intervention purposes. OBJECTIVE: We conducted a point prevalence study to assess ARO rates in long term care homes in Ontario using a secure data collection system. METHODS: All long term care homes in the province were asked to provide colonization or infection counts for methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), and extended-spectrum beta-lactamase (ESBL) as recorded in their electronic medical records, and the number of current residents. Data was collected online during the October-November 2011 period using a Paillier cryptosystem that allows computation on encrypted data. RESULTS: A provably secure data collection system was implemented. Overall, 82% of the homes in the province responded. MRSA was the most frequent ARO identified at 3 cases per 100 residents, followed by ESBL at 0.83 per 100 residents, and VRE at 0.56 per 100 residents. The microbiological findings and their distribution were consistent with available provincial laboratory data reporting test results for AROs in hospitals. CONCLUSIONS: We describe an ARO point prevalence study which demonstrated the feasibility of collecting data from long term care homes securely across the province and providing strong privacy and confidentiality assurances, while obtaining high response rates.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".