Safety of Anti-TNF Treatment in Liver Transplant Recipients: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIM: Little is known about the risk of serious infection when combining anti-tumour necrosis factor [TNF] therapy for refractory inflammatory bowel disease [IBD] with immunosuppression after liver transplantation [LT]. Our aim was to investigate the infection risk in this patient group by systematic review and meta-analysis of the available data. METHODS: A search was conducted for full papers and conference proceedings through September 2015, regarding liver transplant recipients and anti-TNF therapy. All studies were appraised using the adapted Newcastle-Ottawa Scale [NOS]. Two reviewers independently extracted patient data [age, duration of follow-up, number of all infections, number of serious infections, time since transplant]. As an additional control population, primary sclerosing cholangitis [PSC]-IBD patients from the Leiden University Medical Center [LUMC] LT cohort were used. Poisson regression was used to compare serious infections (according to International Conference on Harmonisation [ICH] definition) per patien-year follow-up between the anti-TNF and control groups. RESULTS: In all 465 articles and abstracts were identified, of which eight were included. These contained 53 post-LT patients on anti-TNF therapy and 23 post-LT patients not exposed to anti-TNF therapy. From the LUMC LT-cohort, 41 PSC patients with PSC-IBD not exposed to anti-TNF therapy were included as control population. The infection rate for TNF-exposed patients was 0.168 serious infections per patient year, compared with 0.149 in the control patients (rate ratio 1.12 [95% confidence interval: 0.233-5.404, P = 0.886]. When correcting for time since transplant, the infection rate was 0.194 in the TNF-exposed vs 0.115 in the non-exposed [p = 0.219]. CONCLUSIONS: No significant increase in the rate of serious infection was observed in LT recipients with PSC-IBD during exposure to anti-TNF therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".