Laboratory practices to mitigate biohazard risks during the COVID-19 outbreak: an IFCC global survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objectives: A global survey was conducted by the IFCC Task Force on COVID-19 to better understand how general biochemistry laboratories manage the pre-analytical, analytical and post-analytical processes to mitigate biohazard risks during the coronavirus disease 2019 (COVID-19) pandemic. Methods: An electronic survey was developed to record the general characteristics of the laboratory, as well as the pre-analytical, analytical, post-analytical and operational practices of biochemistry laboratories that are managing clinical samples of patients with COVID-19. Results: A total of 1210 submissions were included in the analysis. The majority of responses came from hospital central/core laboratories that serve hospital patient groups and handle moderate daily sample volumes. There has been a decrease in the use of pneumatic tube transport, increase in hand delivery and increase in number of layers of plastic bags for samples of patients with clinically suspected or confirmed COVID-19. Surgical face masks and gloves are the most commonly used personal protective equipment (PPE). Just >50% of the laboratories did not perform an additional decontamination step on the instrument after analysis of samples from patients with clinically suspected or confirmed COVID-19. A fifth of laboratories disallowed add-on testing on these samples. Less than a quarter of laboratories autoclaved their samples prior to disposal. Conclusions: The survey responses showed wide variation in pre-analytical, analytical and post-analytical practices in terms of PPE adoption and biosafety processes. It is likely that many of the suboptimal biosafety practices are related to practical local factors, such as limited PPE availability and lack of automated instrumentation.
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.
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 | 0.003 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it