The protective efficacy of inactivated vaccine against hemorrhagic fever with renal syndrome: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is a kind of natural epidemic diseases with rodents as the main source of infection. The main clinical manifestations of HFRS are fever, hemorrhage, congestion, hypotensive shock and kidney damage. Some studies showed that vaccination populations in infected areas with inactivated vaccines can reduce the incidence of the disease, but there are variations in protection rates among these studies. The aim of this study is to systematically evaluate the protective effect of inactivated vaccines against HFRS. METHODS: Web of Science, PubMed, SinoMed, Proquest, China National Knowledge Infrastructure Database, Wanfang Database, VIP Database were searched from their inception to December 2024. Newcastle-Ottawa Scale (NOS) was used to assess the quality of evidence, and a random-effects meta-analysis was done to calculate pooled risk ratios for vaccination uptake. All the relevant data were analyzed by using STATA 15.0. RESULTS: A total of 15 articles were included, all of which explicitly reported the total number of vaccinated and unvaccinated people in the vaccination group, and the number of cases that developed during the observation period. Six of these articles reported positive antibody transfer rates. The protection rate of the inactivated HFRS vaccine reached 86%, and a subgroup analysis showed that there was a significant difference in the protection rate of the inactivated vaccine between Korea and China. The positive IgG antibody transfer rate was 97%, and neutralizing antibody transfer rate was 37%. CONCLUSION: The results indicated that inactivated vaccine has a good protective effect against HFRS and should be universally administered to populations in high prevalence areas to control the harm caused by HFRS epidemics.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.049 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".