Surveillance of laboratory exposures to human pathogens and toxins, Canada, 2023
Bibliographic record
Abstract
Background: , and monitors human pathogen and toxin incidents in licensed facilities to minimize exposure impact at the individual and population level. Objective: To provide an overview of confirmed laboratory exposure incidents in Canada in 2023. Methods: Confirmed exposure incident reports in 2023 were analyzed using R 4.2.2, Microsoft Excel and SAS. Results: In 2023, 207 incident reports were received, including 63 confirmed exposure incidents that affected 85 individuals. The academic sector accounted for 50.8% (n=32) of the reported confirmed exposure incidents. Microbiology (n=33; 52.4%) was the predominant activity being performed, with the most common occurrence types being sharps-related (n=22; 27.2%) and procedure-related (n=16; 19.8%). Human interaction (n=36; 57.1%) and standard operating procedures (n=24; 38.1%) were the most frequent root causes cited, with corrective actions often directly addressing these causes. Most of the 85 affected individuals were technicians/technologists (n=55; 64.7%) and had a median of 11 years of laboratory experience. Sixty-seven human pathogens and toxins (HPTs) were implicated in the confirmed exposure incidents, with bacteria (n=36; 53.7%) being the most common biological agent type. The median time between the incident and the reporting date was six days. Conclusion: The number of confirmed exposure incidents increased in 2023 compared to 2022. Microbiology was most often the activity being performed at the time of exposure, and occurrence-types, root causes and HPTs implicated in 2023 mirrored those cited in 2022.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".