Previous BCG vaccination is associated with less severe clinical progression of COVID-19
Bibliographic record
Abstract
BACKGROUND: BCG vaccination, originally used to prevent tuberculosis, is known to "train" the immune system to improve defence against viral respiratory infections. We investigated whether a previous BCG vaccination is associated with less severe clinical progression of COVID-19 METHODS: A case-control study comparing the proportion with a BCG vaccine scar (indicating previous vaccination) in cases and controls presenting with COVID-19 to health units in Brazil. Cases were subjects with severe COVID-19 (O2 saturation < 90%, severe respiratory effort, severe pneumonia, severe acute respiratory syndrome, sepsis, and septic shock). Controls had COVID-19 not meeting the definition of "severe" above. Unconditional regression was used to estimate vaccine protection against clinical progression to severe disease, with strict control for age, comorbidity, sex, educational level, race/colour, and municipality. Internal matching and conditional regression were used for sensitivity analysis. RESULTS: BCG was associated with high protection against COVID-19 clinical progression, over 87% (95% CI 74-93%) in subjects aged 60 or less and 35% (95% CI - 44-71%) in older subjects. CONCLUSIONS: This protection may be relevant for public health in settings where COVID-19 vaccine coverage is still low and may have implications for research to identify vaccine candidates for COVID-19 that are broadly protective against mortality from future variants. Further research into the immunomodulatory effects of BCG may inform COVID-19 therapeutic research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".