Protective role of Bacillus Calmette–Guérin vaccine in Alzheimer's disease progression: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Alzheimer's disease (AD) represents a significant societal challenge, highlighting the need to explore effective prevention and treatment approaches. Recent literature has suggested that Bacillus Calmette-Guérin (BCG) vaccine may be a viable therapeutic option for immune disorders associated with AD due to its immunomodulatory properties and protection against various diseases. Methods: This systematic review aimed to evaluate the association of BCG vaccine in the prevention of AD using six medical-scientific databases. A meta-analytical approach was undertaken to estimate the risk of AD incidence in patients with and without BCG vaccine exposure, followed by subgroup analyses. A risk of bias (RoB) assessment was performed using the Newcastle-Ottawa Scale (NOS). Results: Six cohort studies meeting our inclusion criteria were included (47,947 participants) in the study. From our meta-analysis, intravesical BCG vaccine administration lowered the risk of incidence of AD by 26% in non-muscle-invasive bladder cancer (p < 0.00001). Subgroup analyses showed that BCG vaccination showed a potentially notable preventive effect on AD in older adults (>75 years) and female participants. Conversely, significant heterogeneity in results was observed among male participants and those aged <75 years. The RoB was low in three studies and unclear in the remaining studies. Conclusions: Although our results support the potential benefits of BCG vaccine in preventing AD in specific demographics, we remain cautious about interpreting such results. Further research examining the implications of BCG vaccination for prevention and possible treatment of AD should be undertaken in the future.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".