The Impact of Portal Flow Modulation (PFM) in the A2ALL Cohort Study.
Bibliographic record
Abstract
Background: Single center reports suggest that reduction of portal blood flow may improve the function of small grafts in living donor liver transplantation (LDLT). We sought to confirm and extend these findings in a larger study. Methods: We analyzed prospective observational cohort data from 9 centers with intraoperative portal flow and pressure measurements before and after re-perfusion and PFM. Results: Among 248 LDLTs, 211 (85%) involved the right lobe, and 37 (15%) the left. Median MELD was 13 (IQR 11-18), with a median graft weight to body weight ratio (GWBWR) of 0.98% (IQR 0.79-1.20). Surgical PFM was used 52 times in 47 cases including: splenic artery ligation in 66%, splenectomy in 13%, and portosystemic shunt in 30% of cases. Reasons for PFM included: size 9%, portal pressure 19%, portal flow 19%, arterial flow 2%, or multiple 36%. Subjects undergoing PFM (M+) had lower graft weight (642 vs. 784, p<0.001) and GWBWR (0.85vs. 1.03, p<.0001) and 40% received left lobes (p<0.001). Recipient age, gender, MELD and donor age were comparable. Portal pressure was higher for subjects that received PFM: 19 vs. 14 (p<0.001); portal and arterial flows were not significantly different. Figure 1 demonstrates flow and pressure changes in 21 subjects with measurements before and after PFM. PFM was most effective in reducing portal pressure, with variable impact on portal and arterial flow. Day 7 bilirubin and the rate of graft dysfunction and failure were not significantly different for M+ compared to M-. Conclusions: Our results provide support for the use of PFM in LDLT with small grafts, though further study is required. Substantial variability in the effects of PFM may be due to intraoperative conditions, anatomy and reliability of measurements, though PFM reliably reduced portal pressure consistent with expectations.Figure: No Caption available.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".