The influence of functional warm ischemia time on <scp>DCD</scp> liver transplant recipients’ outcomes
Bibliographic record
Abstract
BACKGROUND: Duration of functional warm ischemia (f-WIT) is thought to have a causal effect on outcomes in controlled donation after circulatory death (DCD) liver transplantation (LT). METHODS: A retrospective cohort study was conducted at five centers. Data were extracted on donor and recipient characteristics, with attention to parameters recorded during withdrawal of life support to in situ cold perfusion. F-WIT was the time elapsed from any of the hemodynamic and oxygenation parameters to the start of in situ cold perfusion. Parameters were as follows: MAP ≤ 50 mm Hg; SBP ≤ 50 mm Hg; and SPO2 ≤ 60%. The primary endpoint was a composite of disseminated ischemic cholangiopathy (IC), primary non-function (PNF), and early graft failure. RESULTS: 35 patients (14%) developed one or more of the primary outcomes. On univariate analysis, older donors and longer WITs were associated with greater likelihood of complications. Of the f-WIT variations analyzed, only f-WIT with SpO2 ≤ 60% was longer among patients with complications. On multivariate analysis, only donor age was a significant predictor of complications. CONCLUSION: This study demonstrates that, of the f-WITs, f-WIT with SpO2 ≤ 60% is most predictive of post-DCD complications. However, results suggest that there may be an alternate etiology for poor outcomes, and that donor age plays a key role.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".