The Equal Neutralizing Effectiveness of BNT162b2, ChAdOx1 nCoV-19, and Sputnik V Vaccines in the Palestinian Population
Bibliographic record
Abstract
Since the beginning of the COVID-19 pandemic, different viral vector-based and mRNA vaccines directed against the SARS-CoV-2 "S" spike glycoprotein have been developed and have shown a good profile in terms of safety and efficacy. Nevertheless, an unbiased comparison of vaccination efficiency, including post-vaccination neutralizing activity, between the different vaccines remains largely unavailable. This study aimed to compare the efficacy of one mRNA (BNT162b2) and two non-replicating adenoviral vector vaccines (ChAdOx1 nCoV-19 and Sputnik V) in a cohort of 1120 vaccinated Palestinian individuals who received vaccines on an availability basis and which displayed a unique diversity of genetic characteristics. We assessed the level of anti-S antibodies and further determined the antibody neutralizing activity in 261 of those individuals vaccinated with BNT162b2a (121), ChAdOx1 (72) or Sputnik V (68). Our results showed no significant difference in the distribution of serum-neutralizing activity or S-antibody serum levels for the three groups of vaccines, proving equivalence in efficacy for the three vaccines under real-life conditions. In addition, none of the eight demographic parameters tested had an influence on vaccination efficacy. Regardless of the vaccine type, the vaccination campaign ultimately played a pivotal role in significantly reducing the morbidity and mortality associated with COVID-19 in Palestine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".