Frequency of Adverse Events Following Q Fever Immunisation in Young Adults
Bibliographic record
Abstract
Q fever is a zoonosis of concern in many countries. Vaccination is the most effective means of prevention, and since 1989, Australia has had a licensed Q fever vaccine, Q-VAX®. This vaccine was also used in the Netherlands in 2011 following the largest recorded Q fever outbreak globally. There is a paucity of available data regarding adverse events following immunisation (AEFI) for young adult females. Such data are important for informing future vaccination recommendations both within Australia and internationally. This study collected Q fever vaccine (Q-VAX®) AEFI data in veterinary and animal science students at Australian universities. Students were enrolled at the time of vaccination and were emailed a link to an online AEFI survey one week later. Of the 60% (499/827) that responded, 85% were female and the median age was 18 years. Local injection site reactions (ISRs) occurred in 98% (95%; CI 96–99%) of respondents, of which 30% (95% CI 24–32%) were severe. Systemic AEFI occurred in 60% (95%; CI 55–64%) of respondents within the seven days following immunisation. Medical attention was sought by 19/499 (3.8%) respondents, of whom one sought treatment at a hospital emergency department. Females were more likely than males to experience any local ISR (odds ratio [OR] 9.3; 95% CI 2.5–33.8; p < 0.001), ISRs of greater severity (OR 2.5; 95% CI 1.5–4.2; p < 0.001), and any systemic AEFI (OR 1.9; 95% CI 1.1–3.1; p = 0.016). These safety data suggest that a high frequency of adverse events following immunisation should be expected in young adults, particularly females. However, the consequences of Q fever disease are potentially far more debilitating.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".