Early Measurement of Indocyanine Green Clearance Accurately Predicts Short-Term Outcomes After Liver Transplantation
Bibliographic record
Abstract
BACKGROUND: There are no accurate tools to predict short-term mortality or the need for early retransplantation after liver transplantation (LT). A noninvasive measurement of indocyanine green clearance, the plasma disappearance rate (PDR), has been associated with initial graft function. METHODS: We evaluated the ability of PDR to predict early mortality or retransplantation after LT. In this observational prospective study, 332 LT were analyzed. Donor, recipient, and intraoperative data were investigated. The ensuing score was prospectively evaluated in a validation cohort of 77 patients. RESULTS: Thirty-three patients reached the main endpoint. By multivariate analysis, the only independent predictors of the endpoint were PDR (odds ratio [OR], 0.85; 95% confidence interval, 0.79-0.92) and international normalized ratio (OR, 1.45; 95% confidence interval, 1.17-1.82). A risk score weighted by the OR was built using cutoff values of 2.2 or greater for international normalized ratio (1 point) and less than 10%/min for PDR (2 points). Four categories (0 to 3) were possible. The risk of early death or retransplantation was associated with the score (0, 4.4%; 1, 6.5%; 2, 12%; and 3, 50%; χ for trend, P < 0.001). The score was also associated with duration of mechanical ventilation and intensive care unit stay. The score had a good diagnostic performance in the validation cohort (sensitivity, 60%; specificity, 95.5%; positive predictive value, 66.7%; negative predictive value, 94.1%). CONCLUSIONS: A simple score obtained within the first day after LT predicts short-term survival and need for retransplantation and may prove useful when selecting diagnostic and therapeutic strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".