The effect of different immunoprophylaxis regimens on post‐transplant cytomegalovirus (CMV) infection in CMV‐seropositive liver transplant recipients
Bibliographic record
Abstract
BACKGROUND: The effects of different immunoprophylaxis regimens on cytomegalovirus (CMV) infection in liver transplant recipients (LTRs) have not been compared. METHODS: In a cohort, we studied 343 CMV-seropositive recipient (R+) and 83 seronegative donor/recipient (D-/R-) consecutive LTRs from 2004 to 2007. Immunoprophylaxis regimens included steroid-only, steroids plus rabbit anti-thymocyte globulin (rATG), and steroids plus basiliximab. Logistic regression analysis, Cox proportional hazards regression model, and log-rank test were performed for multivariate analysis as appropriate. RESULTS: In total, 164 (39%), 69 (16%), and 193 (45%) patients received steroid-only, basiliximab, and rATG immunoprophylaxis, respectively. CMV infection rates were 15.7% (54/343) in CMV R+ LTRs and 2.4% (2/83) in CMV R- LTRs. Among CMV R+ LTRs who received rATG, the use of at least 6 weeks of CMV prophylaxis reduced the rate of CMV infection from 24.4% (19/78) to 11.7% (9/77). In multivariate analysis, CMV R+ vs D-/R- (odds ratio [OR]=13.1, 95% confidence interval [CI]: 1.8-97.2), rATG >3 mg/kg vs steroid-only induction (OR=1.6, 95% CI: 1.1-2.3), and CMV prophylaxis <6 weeks vs ≥6 weeks (OR=2.7, 95% CI: 1.2-6.4) were independently associated with CMV infection. Subgroup analysis in CMV D-/R+ group who received rATG showed that ≥6 weeks of CMV prophylaxis significantly decreased the risk of CMV infection (OR=1.9, 95% CI: 1.1-3.9; P=.03). CONCLUSION: The use of rATG immunoprophylaxis increases the risk of CMV infection in CMV-seropositive LTRs, specifically in the CMV D-/R+ group. Prophylaxis with valganciclovir in this group for at least 6 weeks decreases the risk of CMV infection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".