Nasopharyngeal Samples Management Strategy During the COVID-19 Pandemic: Experience of the Pasteur Institute of Côte d'Ivoire (2020)
Bibliographic record
Abstract
Medical testing laboratories are an essential link in the efficient management of infectious diseases by the identification of the pathogens involved. However, the arrangements for their operation may appear more difficult in times of health crises and raise multiple issues that may compromise the usual level of quality assurance of biological analyses and the response to needs. The smooth running and control of laboratory activities in a health crisis situation requires the implementation of a management system that allows the federation of all available energies. We report here on the experience of the Pasteur Institute of Côte d'Ivoire, in charge of nasopharyngeal samples management during the COVID 19 pandemic, describing the assessment of pre-analytical activities in the first quarter of the crisis (March to May 2020). We then present the implemented strategy and the results obtained from June to September 2020. This article proposes a framework for sharing experiences to contribute to a better preparation of the pre-analytical phase of laboratory samples during health crises.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".