Operational considerations and challenges of biochemistry laboratories during the COVID-19 outbreak: an IFCC global survey
Bibliographic record
Abstract
Objectives: The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Task Force on COVID-19 conducted a global survey to understand how biochemistry laboratories manage the operational challenges during the coronavirus disease 2019 (COVID-19) pandemic. Materials and methods: An electronic survey was distributed globally to record the operational considerations to mitigate biosafety risks in the laboratory. Additionally, the laboratories were asked to indicate the operational challenges they faced. Results: A total of 1210 valid submissions were included in this analysis. Most of the survey participants worked in hospital laboratories. Around 15% of laboratories restricted certain tests on patients with clinically suspected or confirmed COVID-19 over biosafety concerns. Just over 10% of the laboratories had to restrict their test menu or services due to resource constraints. Approximately a third of laboratories performed temperature monitoring, while two thirds of laboratories increased the frequency of disinfection. Just less than 50% of the laboratories split their teams. The greatest reported challenge faced by laboratories during the COVID-19 pandemic is securing sufficient supplies of personal protective equipment (PPE), analytical equipment, including those used at the point of care, as well as reagents, consumables and other laboratory materials. This was followed by having inadequate staff, managing their morale, anxiety and deployment. Conclusions: The restriction of tests and services may have undesirable clinical consequences as clinicians are deprived of important information to deliver appropriate care to their patients. Staff rostering and biosafety concerns require longer-term solutions as they are crucial for the continued operation of the laboratory during what may well be a prolonged pandemic.
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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 | 0.008 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".