Benefits v. risks of COVID-19 vaccination: an examination of vaccination policy impact on the occurrence of myocarditis and pericarditis
Bibliographic record
Abstract
Studies of myocarditis/pericarditis following mRNA COVID-19 vaccines in Hong Kong have been published. Data are consistent with data from other active surveillance or healthcare databases. The mRNA COVID-19 vaccines have been shown to rarely increase risk of myocarditis, with the highest risk among males aged 12-17 after the second dose. An increased risk of pericarditis has also been shown after the second dose, though less common than myocarditis and more evenly distributed among different sex and age groups. Because of the increased risk of post-vaccine myocarditis, Hong Kong implemented a single dose mRNA COVID-19 vaccine policy on September 15, 2021 for adolescents (age 12-17 years). Post-policy, there were no cases of carditis. 40,167 first dose patients did not receive a second dose. This policy was highly successful in the reduction of carditis, but the trade-off is the potential risk of disease and cost to population-level immunity. This commentary brings forward some important global policy considerations.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".