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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".