Adverse events following mass antibiotic prophylaxis during a Group A <i>Streptococcus</i> outbreak in the Canadian Forces Leadership and Recruit School.
Bibliographic record
Abstract
BACKGROUND: (GAS) infection occurred at the Canadian Forces Leadership and Recruit School. A voluntary mass antibiotic prophylaxis (MAP) program was implemented in March 2018, to interrupt an ongoing GAS outbreak, and to prevent future outbreaks. METHODS: Instructors and recruits were offered a one-time intramuscular injection of 1.2 million units penicillin G benzathine (PGB). Individuals with a penicillin allergy were offered azithromycin; 500 mg orally once weekly for four consecutive weeks. Instructors and recruits were also asked to complete a voluntary and anonymous survey one week after receipt of MAP, to detect MAP-related adverse events. RESULTS: MAP was offered to 2,749 individuals; 2,707 of whom agreed to receive it (98.5% uptake). The majority of personnel experienced adverse events in the days following MAP; 92.3% of personnel who received PGB reported localized pain at the injection site, and 70.2% of personnel who received azithromycin reported gastrointestinal symptoms. However, only five cases of serious adverse events were reported, and less than 1% of recruits could not complete their basic military training course because of MAP-related adverse events. CONCLUSION: The MAP program implemented in March 2018 was the first of its kind in the Canadian Armed Forces, and the largest single use of PGB in a defined group in Canada. It resulted in very few serious adverse events and with minimal impact on military recruits' successful completion of recruit training.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".