Surveillance of laboratory exposures to human pathogens and toxins, Canada, 2022
Bibliographic record
Abstract
Background: The Laboratory Incident Notification Canada (LINC) surveillance system was launched in 2015 to monitor the mandated national reporting of laboratory incidents. This report describes the laboratory exposures reported in 2022. Methods: Exposure incidents were analyzed by activity, occurrence, sector, root cause and pathogens/toxins implicated, while affected individuals were analyzed by education, exposure route, role and years of laboratory experience. An analysis of the median number of exposures per month was conducted, and time between the exposure incident date and the date the incident was reported to LINC was examined. Results: Forty confirmed laboratory exposure incident reports were received, with two suspected laboratory-acquired infections. The exposure incident rate per 100 active licences was 3.8, and the number of exposure incidents was highest in September. The majority of exposure incidents involved risk group 2 pathogens (n=27; 63%) and non-security sensitive biological agents (n=36; 84%). Microbiology was the most cited activity occurring during the exposure event (n=20; 50%), and sharps and procedure-related issues were the most common occurrences (n=15; 24.2% each). Most incidents were reported by the academic sector (n=16; 40%). Human interaction was the most common root cause (n=20; 23.8%) and most affected individuals were technicians/technologists (n=68; 73.1%). The median time delay between the incident date and reporting date was 5.5 days. Conclusion: The exposure incident rate was lower in 2022 than in 2021. Incidents related to sharps and standard operating procedures remained the most common occurrence types. The most cited root cause of exposure incidents involved human interaction.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".