Relationship between meteorological factors and the incidence of severe fever with thrombocytopenia syndrome: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Although meteorological factors are connected with severe fever with thrombocytopenia syndrome (SFTS) incidence, available findings have been inconsistent. This study was performed to systematically evaluate the correlation between meteorological factors and SFTS incidence. METHODS: We performed a thorough literature search in PubMed, Web of Science, Embase, Cochrane Library, and Chinese databases from databases initiatives to November 30, 2024. Literature was searched for correlation between meteorological factors and SFTS incidence. Two researchers screened the retrieved literature based on exclusion and inclusion criteria. Finally, data extraction and quality evaluation were carried out for the included literature, and meta-analysis was executed applying the R package (4.4.1). RESULTS: = -0.69, 95%CI -0.78- -0.59, P < 0.001), and the average wind speed was not significantly correlated with SFTS incidence (P > 0.05). CONCLUSIONS: Factors such as temperature, humidity, precipitation, sunshine duration, and atmospheric pressure are related to the incidence of SFTS with a certain lag effect. Future studies on the relationship between meteorological factors and the incidence of SFTS should fully consider human activities and environmental factors, and explore the pathogenesis and transmission mechanisms in greater depth, so as to provide targeted preventive measures. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".