Surveillance of laboratory exposures to human pathogens and toxins: Canada 2018
Bibliographic record
Abstract
BACKGROUND: . The year 2018 marks the third complete year of data. OBJECTIVE: To describe the laboratory exposure and laboratory-acquired infection incidents that occurred in Canada in 2018 compared to previous years, and then by sector, human pathogens and toxins involved, number of affected persons, incident type and root causes. METHODS: Laboratory incidents that occurred in 2018 were reported through the LINC system. The number of laboratory incidents, people exposed and laboratory-acquired infections were compared to previous years, then the incidents were analyzed by sector, human pathogen or toxin involved, the type of incident, people exposed, route of exposure and root causes. Microsoft Excel 2016 was used for descriptive analysis. RESULTS: In 2018, there were 89 exposure incidents to human pathogens and 235 people were exposed. There were five suspected and one confirmed laboratory-acquired infections. This was approximately twice the number of exposure incidents that were reported in 2017 (n=44) and 2016 (n=46). The highest number of exposure incidents occurred in the academic and hospital sectors, and the ratio of incidence to licences was the lowest in the private sector. The majority of incidents (n=50; 56%) involved Risk Group 2 human pathogens that were manipulated in a Containment Level 2 laboratory. Most exposures were related to sharps or procedures and the most common people exposed were laboratory technicians. Human interaction and standard operating procedures were the leading root causes. CONCLUSION: Although overall the annual incidence of laboratory exposures in Canada remains relatively low, the incidence was higher in 2018 than in previous years. Whether this is a true increase in incidence or an increase in reporting is not known at this time as baseline estimates are still being established.
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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".