Surveillance of laboratory exposures to human pathogens and toxins, Canada 2020
Bibliographic record
Abstract
BACKGROUND: . The objective of this report is to describe laboratory exposures that were reported in Canada in 2020 and the individuals who were affected. METHODS: Laboratory incident exposures occurring in licensed Canadian laboratories in 2020 were analyzed. The exposure incident rate was calculated and the descriptive statistics were performed. Exposure incidents were analyzed by sector, activity type, occurrence type, root cause and pathogen/toxin. Affected persons were analyzed by education, route of exposure sector, role and laboratory experience. The time between the incident and the reporting date was also analyzed. RESULTS: Forty-two incidents involving 57 individuals were reported to Laboratory Incident Notification Canada in 2020. There were no suspected or confirmed laboratory acquired infections. The annual incident exposure rate was 4.2 incidents per 100 active licenses. Most exposure incidents occurred during microbiology activities (n=22, 52.4%) and/or were reported by the hospital sector (n=19, 45.2%). Procedural issues (n=16, 27.1%) and sharps-related incidents (n=13, 22.0%) were the most common occurrences. Most affected individuals were exposed via inhalation (n=28, 49.1%) and worked as technicians or technologists (n=36, 63.2%). Issues with standard operating procedures was the most common root cause (n=24, 27.0%), followed by human interactions (n=21, 23.6%). The median number of days between the incident and the reporting date was six days. CONCLUSION: The rate of laboratory incidents were lower in 2020 than 2019, although the ongoing pandemic may have contributed to this decrease because of the closure of non-essential workplaces, including laboratories, for a portion of the year. The most common occurrence type was procedural while issues with not complying to standard operating procedures and human interactions as the most cited root causes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".