Management of a global health crisis: first COVID-19 disease feedback from Overseas and French-speaking countries medical biologists
Bibliographic record
Abstract
The French society of clinical biology "Biochemical markers of COVID-19" has set up a working group with the primary aim of reviewing, analyzing and monitoring the evolution of biological prescriptions according to the patient's care path and to look for markers of progression and severity of the disease. This study covers all public and private sectors of medical biology located in metropolitan and overseas France and also extends to the French-speaking world. This article presents the testimonies and data obtained for the "Overseas and French-speaking countries" sub-working group made up of 45 volunteer correspondents, located in 20 regions of the world. In view of the delayed spread of the SARS-CoV-2 virus, the overseas regions and the French-speaking regions have benefited from feedback from the first territories confronted with COVID-19. Thus, the entry of the virus or its spread in epidemic form could be avoided, thanks to the rapid closure of borders. The overseas territories depend very strongly on air and/or sea links with the metropolis or with the neighboring continent. The isolation of these countries is responsible for reagent supply difficulties and has necessitated emergency orders and the establishment of stocks lasting several months, in order to avoid shortages and maintain adequate patient care. In addition, in countries located in tropical or intertropical zones, the diagnosis of COVID-19 is complicated by the presence of various zoonoses (dengue, Zika, malaria, leptospirosis, etc.).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".