Cirrhosis and fungal infections‐a cocktail for catastrophe: A systematic review and meta‐analysis with machine learning
Bibliographic record
Abstract
Abstract Objectives We evaluated the magnitude and factors contributing to poor outcomes among cirrhosis patients with fungal infections (FIs). Methods We searched PubMed, Embase, Ovid and WOS and included articles reporting mortality in cirrhosis with FIs. We pooled the point and relative‐risk (RR) estimates of mortality on random‐effects meta‐analysis and explored their heterogeneity (I 2) on subgroups, meta‐regression and machine learning (ML). We assessed the study quality through New‐Castle‐Ottawa Scale and estimate‐asymmetry through Eggers regression. (CRD42019142782). Results Of 4345, 34 studies (2134 patients) were included (good/fair/poor quality: 12/21/1). Pooled mortality of FIs was 64.1% (95% CI: 55.4–72.0, I 2: 87%, p < .01), which was 2.1 times higher than controls (95% CI: 1.8–2.5, I 2:89%, p < .01). Higher CTP (MD: +0.52, 95% CI: 0.27–0.77), MELD (MD: +2.75, 95% CI: 1.21–4.28), organ failures and increased hospital stay (30 vs. 19 days) were reported among cases with FIs. Patients with ACLF (76.6%, RR: 2.3) and ICU‐admission (70.4%, RR: 1.6) had the highest mortality. The risk was maximum for pulmonary FIs (79.4%, RR: 1.8), followed by peritoneal FIs (68.3%, RR: 1.7) and fungemia (55%, RR: 1.7). The mortality was higher in FIs than in bacterial (RR: 1.7) or no infections (RR: 2.9). Estimate asymmetry was evident (p < 0.05). Up to 8 clusters and 5 outlier studies were identified on ML, and the estimate‐heterogeneity was eliminated by excluding such studies. Conclusions A substantially worse prognosis, poorer than bacterial infections in cirrhosis patients with FIs, indicates an unmet need for improving fungal diagnostics and therapeutics in this population. ACLF and ICU admission should be included in the host criteria for defining IFIs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".