Adapting to the Coronavirus Pandemic: Building and Incorporating a Diagnostic Pipeline in a Shared Resource Laboratory
Bibliographic record
Abstract
In March 2020, with lockdown due to the coronavirus pandemic underway, the Francis Crick Institute (the Crick) regeared its research laboratories into clinical testing facilities. Two pipelines were established, one for polymerase chain reaction and the other for Serology. This article discusses the Cricks Flow Cytometry Science Technology Platform (Flow STP) role in setting up the Serology pipeline. Pipeline here referring to the overarching processes in place to facilitate the receipt of human sera through to a SARs-CoV-2 enzyme-linked immunosorbent assay result. We examine the challenges that had to be overcome by a research laboratory to incorporate clinical diagnostics and the processes by which this was achieved. It describes the governance required to run the service, the design of the standard operating procedures (SOPs) and pipeline, the setting up of the assay, the validation required to show the robustness of the pipeline and reporting the results of the assay. Finally, as the lockdown started to ease in June 2020, it examines how this new service affects the daily running of the Flow STP. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".