Multi-variate statistical and machine learning reveals the interplay between sex and age in antibody responses to <i>de novo</i> SARS-CoV-2 infection and vaccination
Bibliographic record
Abstract
Summary Prevention of negative COVID19 infection outcomes and infection/vaccine-acquired immunity is associated with the quality of antibody responses, whose variance by age and sex are poorly understood. Integrated, network approaches, identified sex and age effects in antibody responses and neutralization potential of de novo infection and vaccination throughout the Covid-19 pandemic. Cluster analysis found neutralization values followed SARS-CoV-2 specific receptor binding RIgG, spike SIgG and S and RIgA levels based on COVID19 status. Stochastic behavior tests and other analytical methods revealed sex differences only in persons <40y.o. Serum IgA antibody titers correlated with neutralization only in females 40-60y.o. Network analysis found males could improve IgA responses after vaccination dose 2, unlike >60y.o. females. Complex correlation analyses found vaccination induced less antibody isotype switching and neutralization in older persons, especially in females. Sex dependent antibody & neutralization behavior decayed fastest in older males and with vaccination. Such sex and age characterization by machine learning can direct studies integrating cell mediated responses to define yet elusive correlates of protection and inform age and sex precision-focused vaccine design.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".