Comparative IgG Antibody Titers Following Second Dose of Sinopharm and Pfizer Vaccination
Bibliographic record
Abstract
Background and Objective: The vaccines Sinopharm and Pfizer account for more than 7.3 billion vaccinations across the globe. Study data shows that the protection offered by these vaccines wanes with time, which is why the third dose of a different or same vaccine may become necessary. The objective of this study was to compare the levels of IgG in the post-vaccination phase, with two different vaccines, Sinopharm and Pfizer. Methodology: A cross sectional study was conducted at CMH Lahore Medical College and Institute of Dentistry, after approval by the Ethical committee. A total of 100 participants, who were completely vaccinated, with either Sinopharm or Pfizer, at least six weeks before, were included in the study. Participants with concurrent infection, incomplete vaccination or any known disease, were excluded from the study. Written consent was obtained from all the participants. A predesigned questionnaire, adapted from similar studies, was used for data collection. Afterwards, blood samples were collected and IgG antibody levels were estimated using RD-RatioDiagnostics SARS-COV-2 virus IgG ELISA kit (E-COG-K105). The collected data was analyzed with SPSS software. Results with p value < 0.05 were taken as significant. Results: Mean age of the participants was 20.18±1.29 years. Mean antibody titers, six weeks post-vaccination, were 5453.73±609.15 U/ml and 10786.86±1525.49 U/ml in Sinopharm and Pfizer groups, respectively. The difference was statistically significant (p=0.0004). Conclusion: Antibody response is considerably higher in Pfizer-vaccinated individuals, in comparison to Sinopharm.
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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 | 0.001 | 0.001 |
| 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".