Canadian laboratory incidents with human pathogens and toxins: An overview of reports, 2016–2022
Bibliographic record
Abstract
Background: came into force, the reporting of laboratory incidents to the Laboratory Incident Notification Canada (LINC) surveillance system became mandatory. This report summarizes the laboratory exposure and non-exposure data reported from 2016 to 2022, with a particular focus on factors that are not typically presented in LINC's annual report. Methods: Reported laboratory incidents from 2016 to 2022 were analyzed. Exposures were analyzed by severity, occurrence and root cause, and affected individuals were analyzed by disease outcome, role and applied interventions. Non-exposures were analyzed by incident type. Exposure and non-exposure incident rates were calculated. Results: Events reported to LINC totalled 928. Of those, 355 were confirmed non-exposures, 361 were confirmed exposures, and 111 were other events. Both exposure and non-exposure incident rates per 100 active licences peaked in 2018 (9.44 and 7.11, respectively). Most exposures were rated as minor or negligible severity. The most cited exposure occurrence types were sharps-related and procedure-related (23% each), and standard operating procedure-related root causes were most cited (24%). While 781 individuals were affected in the exposure incidents, most did not develop a laboratory-acquired infection (n=753; 96%) and received at least one form of treatment post-exposure (n=717; 92%). Inadvertent possession/production cases were the most common non-exposure incidents reported. Conclusion: Exposure and non-exposure incident rates have decreased since 2018. Among exposure incidents, sharps-related and procedure-related occurrences were the most common, and the root cause was usually a standard operating procedure. Non-exposure incidents were mostly inadvertent possession/production cases. Exposure and illness outcome severity was mostly minor.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".