The protective immunity induced by SARS-CoV-2 infection and vaccination: a critical appraisal
Bibliographic record
Abstract
Understanding the interactions of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) with humans is deeply grounded in immunology, from the diagnosis to pathogenesis, from the clinical presentations to the epidemiology, prevention, and treatment. However, the difficulty of capturing the complex and changeable array of immunological concepts and incorporating them into the strategies of control of the SARS-CoV-2 pandemic poses significant hindrances to establish optimal public health policies. The contribution of immunology to the control of the pandemic is to shed light on the features and mechanisms of the protective immunity elicited by SARS-CoV-2 infection and vaccines. Do they induce effective protective immunity? How? For how long? What is the effect of vaccination on individuals who were previously infected? To appropriately answer these questions, it is necessary to get rid of the outdated notion of a naïve, static, and closed immune system, which leads to misconceptions about susceptibility, specificity, immunological memory, and protective immunity. The present essay discusses these issues based on current immunological concepts.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".