Healthcare utilization among COVID-19 mRNA vaccine-associated myocarditis cases: a matched retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: We evaluated all-cause healthcare utilization among those with vaccine-associated myocarditis, compared to vaccinees without postvaccination myocarditis. METHODS: We conducted a retrospective cohort study in individuals aged 12 and older who received COVID-19 mRNA vaccination in British Columbia. Exposure was defined as an ED visit or hospitalization for myocarditis within 21 days postvaccination. The primary outcome was healthcare utilization. Ratios of rate ratios (RRRs) for exposure-associated healthcare utilization were calculated using a difference-in-differences (DiD) analysis. RESULTS: In the postindex period, the exposed and unexposed groups showed substantial utilization rate difference (RD = 15.30 [95% CI, 14.47-16.13). A 51% overall increase in healthcare utilization was observed over 18 months among exposed individuals (RRR, 1.51 [95%CI, 1.08-2.11]). In the initial six months, healthcare utilization surpassed the 18-month estimate, exhibiting a 125% increase (RRR, 2.25 [95%CI, 1.43-3.52]), while the last 12 months showed no statistically significant change (RRR, 1.03 [95%CI, 0.72-1.47]). An additional 9.1 (95%CI, 8.53-9.71) visits per person were attributed to vaccine-associated myocarditis over 18 months (total excess = 938.26 healthcare visits). CONCLUSION: The initial surge in healthcare visits postexposure, mainly outpatient follow-ups, followed by a return to baseline rates, indicates a positive prognosis and supports the vaccine's safety profile.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".