Serum Factor V Is a Continuous Biomarker of Graft Dysfunction and a Predictor of Graft Loss After Liver Transplantation
Bibliographic record
Abstract
BACKGROUND: Factor V has never been compared to a validated early allograft dysfunction (EAD) definition. We aimed to assess factor V as a biomarker of EAD and a predictor of graft loss after liver transplantation (LT). METHODS: We retrospectively assessed the serum factor V levels on postoperative day 1 after LT. Patients were divided according to their factor V levels into the ≤36.1 U/mL and > 36.1 U/mL groups. The primary outcome was graft loss within 1, 3, and 6 months. The secondary outcome was EAD, as defined by Olthoff et al. Predictors of outcomes were identified by multivariable logistic regression. RESULTS: Two hundred twenty-seven patients were included in the study: 74 with factor V of 36.1 U/mL or less and 153 with factor V >36.1 U/mL. EAD was diagnosed in 41 (55.4%) of 74 patients with factor V of 36.1 U/mL or less and in 20/153 (13.1%) patients with factor V >36.1 U/mL (P < 0.001). According to the multivariable regression model, factor V was a continuous marker of EAD (odds ratio [OR], 0.96; 95% confidence interval [CI], 0.94-0.98 per U/mL). Among the study groups, the 1-, 3-, and 6-month graft survival rates were 82%, 74%, and 74%, respectively, for patients with factor V of 36.1 U/mL or less and 98%, 95%, and 95%, respectively, for patients with factor V >36.1 U/mL (P = 0.001). Factor V was a continuous predictor for 3- and 6-month graft losses (OR, 0.96; 95% CI, 0.94-0.99 and OR, 0.97; 95% CI, 0.94-0.99 per U/mL), whereas EAD was not significant when adjusted for factor V. CONCLUSION: Factor V is an early marker for EAD and is a continuous predictor of short-term graft loss after LT.
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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) | 0.001 | 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 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".