Cluster analysis of carbapenemase-producing organisms in Alberta using a geographic information system
Bibliographic record
Abstract
Background: The international emergence of carbapenemase-producing organisms (CPOs) among gram-negative bacteria represents a public health threat. In Canada, CPO rates have been increasing and there is growing concern of CPO reservoirs that may exist outside of hospital settings. The geographic patterns of CPOs were investigated in Alberta, Canada using geographic information systems and spatial analysis was performed to determine the presence of any spatial clusters of CPOs. Methods: Using multiple healthcare data sources, CPO case information identified in the community and healthcare settings were collected. The CPO cases were analyzed using spatial scan statistics to detect any CPO spatial clusters within the province of Alberta. The identified spatial clusters were stratified based on the collected healthcare data from the surveillance management system. Results: Spatial analysis confirmed two CPO spatial clusters, specifically within the municipalities of Edmonton and Calgary. The spatial cluster in southeast Edmonton had 27 CPO cases with a relative risk of 9.06 (p < .001). The spatial cluster in northeast Calgary had 22 CPO cases with a relative risk of 6.24 (p < .001). The NDM gene was the primary gene type in both spatial clusters and there was a higher proportion of cases that had previous history of travel outside of Alberta without healthcare exposure. The Serratia marcescens enzyme gene was observed to have the second highest proportion in all the CPO cases in the province, however, it was not well represented in either spatial cluster. Conclusions: The study confirmed spatial patterns of CPO distribution in Alberta. The findings can direct additional studies and may be used to develop community-specific interventions or strategies to prevent the transmission of CPO in Alberta.
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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 | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".