A 6-week time period may not be sufficient to identify potential adverse events following COVID-19 vaccination
Bibliographic record
Abstract
Abstract Background . Messenger RNA (mRNA) vaccines have been widely used as the main sanitary measure destined to fight the COVID-19 pandemic. Rapidly purported as being “safe and effective”, this new generation of vaccines is radically different from those developed traditionally and for which potentially associated adverse events (AEs) are considered through a standard 6-week post-vaccination period. Hypothesis . Here, we posited that the reporting period for AEs related to the COVID-19 vaccines may be different. Method . In this retrospective, observational study, we aimed to assess the chronology of new/worsening ailments occurring after the administration of COVID-19 vaccines based on the changes to the participants’ pharmacological records. Patients vaccinated against COVID-19 and experiencing health-related events during the study period (between September 30, 2021 and July 15, 2022) were included and the changes to their pharmacological records were analyzed. Results . One hundred and twelve (112) adult patients (63 men, 49 women; 67.54 ± 14.55 years-old; mean ± standard deviation) have reported changes to their pharmacological record following health-related events, which occurred 11.57 weeks (median; range 0.04–47.14) following their last COVID-19 injection of 3 doses (median; range 1–4). The most frequent medical ailments that appeared or worsened were cardiovascular diseases (CVD; N = 61), cancer (N = 31), respiratory diseases (RD; N = 22) and zona (N = 10), half of which occurred after the second dose. Nineteen (19) patients (10 men, 9 women; 78.2 ± 11.4 years-old) died on average 17.14 weeks (SD 13.71) after their last injection. Conclusion . Most (76.1%) of the health-related events experienced by patients vaccinated against COVID-19 occurred beyond the 6-week period prescribed by the health authorities. Our findings call for further investigations and an extension of the post-vaccination AE reporting period.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".