The SeroNet Clinical and Translational Serology Task Force (CTTF) SARS-CoV-2 mucosal immunity methodological considerations and best practices workshop
Bibliographic record
Abstract
SARS-CoV-2 persists in certain populations, even with vaccination and boosters. Emerging evidence suggests that reductions in virus transmission and infection will likely require involvement of the mucosal immune system, especially secretory antibodies in the upper respiratory tract. The Clinical and Translational Serology Task Force (CTTF) within The National Cancer Institute (NCI)'s Serological Sciences Network for COVID-19 (SeroNet) hosted a workshop to review the status of development and standardization of mucosal sample collection methods and assays, identify challenges, and develop action plans to bridge gaps. Speakers presented data underscoring a role for secretory IgA in protection, mucosal markers as correlates of protection, methods for tracking and assessing mucosal antibodies, and lessons learned from other infectious agents. Perspectives from regulators and industry were put forward to guide mucosal vaccine development. Methodological considerations for optimizing collection protocols and assays and harmonizing data were highlighted. Rigorous studies, standardized protocols, controls, standards, and assay validation were identified as necessary to gain momentum in expanding SARS-CoV-2 vaccines to the mucosa.
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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 | 0.244 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".