Incidence rate of hemorrhagic fever with renal syndrome complicated with acute pancreatitis: a meta-analysis
Bibliographic record
Abstract
Background: Acute pancreatitis (AP) is a rare but serious complication in patients diagnosed with hemorrhagic fever with renal syndrome (HFRS). When AP complicates HFRS, the clinical outcome significantly worsens and the risk of mortality increases. However, the incidence of AP in HFRS patients and its associated mortality risk remain unclear. To address this knowledge gap, we conducted a meta-analysis to determine the AP incidence rate in HFRS patients and assess the impact of AP on mortality in these patients. Methods: We systematically searched seven databases (PubMed, Web of Science, EMBase, Sinomed, Chinese National Knowledge Infrastructure, WanFang Data, and Chongqing VIP) for relevant studies on HFRS complicated by AP. The studies were selected using predefined inclusion and exclusion criteria based on the Population, Intervention, Comparison, Outcome, and Study design principle. Two independent reviewers screened the studies, and the quality of the included studies was assessed using the Agency for Healthcare Research and Quality and the Newcastle-Ottawa Evaluation Scale (NOS). Results: 1.112-12.031). No statistically significant differences were observed in the subgroup and meta-regression analyses. Conclusion: Although the incidence of AP in HFRS patients is not high, it significantly increases the risk of mortality in these patients. Future large-scale prospective studies are required to further validate these findings.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.032 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".