THE ECONOMIC IMPACT OF CYTOMEGALOVIRUS INFECTION AFTER LIVER TRANSPLANTATION
Bibliographic record
Abstract
BACKGROUND: We studied the economic impact of cytomegalovirus (CMV) disease and its effective reduction with antiviral prophylaxis in liver transplant recipients. METHOD: Analysis of institutional charge data accumulated during a prospective, randomized, controlled trial comparing oral acyclovir 800 mg four times daily for 120 days (ACV) and intravenous ganciclovir 5 mg/kg every 12 h for 14 days followed by ACV for 106 days (GCV) was performed. RESULTS: Liver transplant recipients who developed CMV disease had significantly higher charges (median: $148,300) than those who developed asymptomatic CMV infection ($119,600) or experienced no CMV infection ($114,100) (P<0.01). A multiple linear regression analysis indicated that CMV disease is associated with a 49% increase in charges, independent of other factors influencing increased hospitalization charges. In CMV-seronegative patients who received a CMV-seropositive donor organ, GCV prophylaxis was associated with a significant reduction in charges, as compared to ACV prophylaxis ($113,900 vs. $153,300, respectively; P=0.02). CONCLUSIONS: CMV disease is an independent risk factor for increased resource utilization associated with liver transplantation. The use of an effective prophylactic antiviral regimen provides savings in health care resources, particularly in patients at high risk for developing CMV disease.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".