A Tale of Two Countries: DiaSorin Molecular’s Rapid Response to the COVID-19 Pandemic
Bibliographic record
Abstract
In the summer of 2019, DiaSorin Molecular started designing a multiplex respiratory panel with pan-coronavirus detection as one of the planned targets. The R&D team in Gerenzano, Italy was already searching databases, performing alignments and assessing preliminary target regions for common coronavirus RT-PCR, including SARS and MERS-CoV. In December 2019, we were vigilant and following a cluster of pneumonia cases with undetermined etiology in Wuhan, China. As we now know, the cause of the respiratory infections was the new SARS-CoV-2 virus. DiaSorin Molecular swiftly responded in line with our heritage and company history in detecting emerging infectious diseases. Early in the pandemic and in record time, using research and development teams in both Italy and the U.S. together with the U.S. manufacturing team, we were able to develop and commercialize a new diagnostic test, Simplexa™ COVID-19 Direct, to detect SARS-CoV-2. Our unique platform allowed development of a rapid diagnostic test without the need for extraction reagents. Challenges with control materials, quarantines, clinical samples, raw materials and production were overcome and the entire company worked side by side for accelerated delivery of this assay to clinical labs in Europe, the U.S. and Canada.
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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 | 0.025 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".