Breakthrough Infection and Death after COVID-19 Vaccination: A Physics Perspective
Bibliographic record
Abstract
The largest universal immunization in history has occurred as a result of the COVID-19 pandemic. The developed COVID-19 vaccines have been shown to provide protection against severe forms of COVID-19 by inducing anti-spike neutralizing antibodies. It has been found that individuals who have not been vaccinated against COVID-19 were more likely to contract the virus during a period when the Delta variant was dominant, as compared to those who have received the complete dose of the vaccine, irrespective of the variant. However, there is no notable disparity in the likelihood of hospitalization, requirement for mechanical ventilation, or mortality between the two groups once infected. Nevertheless, those who are unvaccinated may require additional oxygen support. There are reports indicating unfavorable health effects, ranging from transient thyroid dysfunction to death following vaccination. In addition, some people are susceptible to SARS-CoV-2 infection despite they have immunized with the COVID-19 vaccine. Given all these considerations, several key factors should be better understood and considered to enable us to even more successfully manage future pandemics breakthrough infections. The effectiveness of physical treatment methods, e.g., Low Dose Radiation Therapy (LDRT) should be compared to pharmacological treatments.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".