Pancreatic Fungal Infection in Patients With Necrotizing Pancreatitis
Bibliographic record
Abstract
GOAL: The goal of this study was to study the incidence of fungal infection in necrotizing pancreatitis (NP) and its impact on mortality. BACKGROUND: Infected pancreatic necrosis is a major contributor to morbidity and mortality in patients with NP. While pancreatic fungal infection (PFI) has frequently been identified in patients with NP, its effect on the clinical outcomes is unclear. MATERIALS AND METHODS: A literature search was performed in Medline (Ovid), Embase (Ovid), and the Cochrane library. All prospective and retrospective studies that examined the incidence of fungal infection in NP with subgroup mortality data were included. For fungal infection of NP, studies with fungal isolation from pancreatic necrotic tissue were included. Newcastle Ottawa Scale and Joanna Briggs Institute's critical appraisal tool were used for bias assessment. RESULTS: Twenty-two studies comprising 2151 subjects with NP were included for the quantitative analysis. The mean incidence of fungal infection was 26.6% (572/2151). In-hospital mortality in the pooled sample of NP patients with PFI (N=572) was significantly higher [odds ratio (OR)=3.95, 95% confidence interval (CI): 2.6-5.8] than those without PFI. In a separate analysis of 7 studies, the mean difference in the length of stay between those with and without fungal infection was 22.99 days (95% CI: 14.67-31.3). The rate of intensive care unit admission (OR=3.95; 95% CI: 2.6-5.8), use of prophylactic antibacterials (OR=2.76; 95% CI: 1.31-5.81) and duration of antibacterial therapy (mean difference=8.71 d; 95% CI: 1.33-16.09) were all significantly higher in patients with PFI. Moderate heterogeneity was identified among the studies on estimating OR for mortality (I2=43%) between the 2 groups. CONCLUSIONS: PFI is common in patients with NP and is associated with increased mortality, intensive care unit admission rate, and length of stay. Further prospective studies are needed to better understand the pathophysiology of PFIs and to determine the role for preemptive therapeutic strategies, such as prophylactic antifungal therapy.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".