Point-of-Care Testing Biosafety Decisions: An Investigation Summary Illustrating Current Decision-Making Process in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Point-of-care testing (POCT) is increasingly being used in healthcare, including hospitals, and POCT-style tests are also used within some laboratories. The principles of biosafety, including risk assessment and containment of biohazardous agents, can be utilized as a foundation to establish policies and procedures guiding safe performance of POCT. However, specific biosafety guidelines for POCT are generally lacking, particularly for those performed outside laboratories by healthcare workers. This study aims to explore POCT biosafety program decision-making infrastructure and oversight in Ontario. CONTENT: The Institute of Quality Management in Healthcare distributed a survey to 249 laboratories in Ontario. There were 11 questions on POCT biosafety practices. SUMMARY: The survey had a high response rate of 88.7%. How POCT biosafety decisions were made was variable among respondents. For POCT-style tests conducted within laboratories, the biosafety officer (BSO) and/or the microbiologist were involved in biosafety decisions in 95% of microbiology labs or 55% of other labs. Only 27% of the respondents reported that biosafety decisions were made by BSOs and/or microbiologists when POCT was conducted outside the laboratory. When POCT is performed outside the laboratory, biosafety decisions were made largely by Infection Prevention and Control (IPAC) and POCT laboratory staff. Similarly, training and auditing of staff who perform POCT were mainly done by IPAC and POCT laboratory staff. The survey showed that a wide variety of POCT was being conducted for COVID-19 patients during the pandemic.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".