Effect of Vaccination Status on SARS-CoV-2 Antibody Levels in Gowa Regency Community, Indonesia
Bibliographic record
Abstract
Background: COVID-19 is a disease caused by infection with SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). One of the body's immune responses to infection is to produce antibodies. Acute SARS-CoV-2 infection initiates cellular and humoral immune responses. The humoral immune response specifically generates antibodies against virus-specific antigens. Several factors influence the immune response, one of which is vaccination status. Therefore, this study aimed to determine and analyze the effect of vaccination status on SARS-CoV-2 antibody levels. Methods: An analytical observational study with a cross-sectional design involving 815 samples was conducted. The proportional random sampling technique was employed based on data obtained from the Seroepidemiology Survey. Data analysis was conducted using the STATA version 14.0 program with the Independent T-Test, Mann Whitney test, Kruskal Wallis test, and Multiple logistic regression. Results: The results showed that there was a significant relationship between the determinant variables of SARS-CoV-2 antibody levels based on gender (p=0.012), vaccination status (p=0.000), and COVID-19 infection history (p=0.000). Furthermore, the multivariate analysis indicated that vaccination status was the variable most associated with SARS-CoV-2 antibody levels (p = 0.010). The OR value = 0.16 < 1 and 95%CI (0.04-0.65) which did not contain a value of 1 suggested vaccination status to be a significant protective factor associated with SARS-CoV-2 antibody levels, with a probability value of 94.1%. Conclusion: The most influential variable on SARS-CoV-2 antibody levels in the Gowa Regency was vaccination status. Moreover, none of the variables measured were identified as confounding factors or showed interaction effects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".