Surveillance of laboratory exposures to human pathogens and toxins: Canada 2017
Bibliographic record
Abstract
BACKGROUND: , the Public Health Agency of Canada (PHAC) is mandated with monitoring laboratory incident notifications through the Laboratory Incident Notification Canada (LINC) surveillance system. The year 2017 marks the second complete year of data. OBJECTIVE: To describe the laboratory exposure and laboratory-acquired infection incidents that occurred in Canada in 2017 by sector, human pathogens and toxins involved, number of affected persons, incident type and root causes. METHODS: The incidents included in the analysis occurred between January 1 and December 31, 2017. They were reported by laboratories with active licences to PHAC through the LINC surveillance system. Microsoft Excel 2010 was used for basic descriptive statistics. RESULTS: (n=3; 7%) were the most frequently involved in reported exposure incidents. These two pathogens are both RG3 and security-sensitive biological agents (SSBAs). An average of 2.3 root causes were identified per incident (n=101). Problems with standard operating procedures (SOPs) and human error were the two most common causes. CONCLUSION: The incidence of laboratory exposure incidents was relatively low in 2017. The most common route of exposure was through inhalation and the most common root causes were problems with SOPs and human error. Since this is a new surveillance system, baseline estimates are still being established.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".