Systematic review with meta‐analysis: risk factors for recurrent primary sclerosing cholangitis after liver transplantation
Bibliographic record
Abstract
BACKGROUND: After liver transplantation primary sclerosing cholangitis (PSC), the condition returns in the transplanted liver in a subset of patients (recurrent primary sclerosing cholangitis, rPSC). AIM: To define risk factors for rPSC. METHODS: We searched Pubmed, Embase, Web of Science, and Cochrane library for articles published until March 2018. Studies addressing risk factors for developing rPSC were eligible for inclusion. A random effects meta-analysis was conducted using hazard ratios (HR) as effect measure. Study quality was evaluated with the Newcastle Ottawa scale. Statistical analysis was performed using Cochrane Review Manager. RESULTS: The electronic database search yielded 449 results. Twenty-one retrospective cohort studies met the inclusion criteria for the review; 14 were included in the meta-analysis. The final cohort included 2159 patients (age range 31-49 years, 68.8% male), of whom 17.7% developed rPSC. Colectomy before liver transplantation, HR 0.65 (95% CI: 0.42-0.99), cholangiocarcinoma before liver transplantation, HR 2.42 (95% CI: 1.20-4.86), inflammatory bowel disease, HR 1.73 (95% CI: 1.17-2.54), donor age, HR 1.24 (95% CI 1.0-1.45) per ten years, MELD score, HR 1.05 (95% CI: 1.02-1.08) per point and acute cellular rejection, HR of 1.94 (95% CI: 1.32-2.83) were associated with the risk of rPSC. CONCLUSIONS: Multiple risk factors for rPSC were identified. Colectomy before liver transplantation reduced the risk of rPSC.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.044 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".