Work within the COVID-19 pandemic — the experience of the biobank of the National Medical Research Center of Oncology
Bibliographic record
Abstract
Aim. To present the main results and changes in the work of the biobank of the National Medical Research Center of Oncology during the pandemic of coronavirus disease 2019 (COVID-19). Material and methods. The paper presents a dynamic analysis of the delivery of fresh frozen biological samples from operated patients for three quarters of 2019 and 2020, as well as considers possible ways to implement research projects to collect and deposit materials for the biobank within the COVID-19 pandemic. The work included persons over 18 years old, with primary gastrointestinal cancers, who, upon hospitalization, gave informed consent to transfer biological material to the biobank. One of the inclusion criteria was the presence of a negative nasopharyngeal swabs tested for SARS-CoV-2 by the polymerase chain reaction. Data calculation and comparative analysis of the results was carried out using the Microsoft Office Excel software package. Results. It was revealed that in the first quarter of 2019, 34% of biological samples were received from the total amount for the year, while in 2020 — 50%; in the second quarter of 2019 — 35%. The second quarter of 2020 was characterized by change in the schedule of work of institutions due to the COVID-19 pandemic, which led to a 56% decrease in the number of samples compared to the same period in 2019 and amounted to 14% of material collected for the three quarters of2020. In the third quarter of 2020, the flow was restored and amounted to 65 patients, which corresponds to 36% of material collected in this year and is more than in 2019 by 23%. Conclusion. a critical decrease in the deposited material in the second quarter of 2020 indicated the need to adapt the current biobanking rules inRussia in general and the studied biobank in particular. Possible adaptation ways may consist in the creation of joint projects between groups of scientists from different organizations, taking into account the requirements of information and biological safety. This problem and ways to solve it were widely discussed at international and Russian platforms, including the 4th meeting of the National Association of Biobanks and Biobanking Specialists, dedicated to the organization of biobanking during the COVID-19 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".