Stratifying risk in ACLF-3 patients: The impact of circulatory and respiratory failure on one-year post-transplant outcomes
Bibliographic record
Abstract
BACKGROUND: In patients with Acute-on-Chronic Liver Failure grade 3 (ACLF-3), the number of organ failures (OF) before liver transplant (LT) is associated with poorer outcomes following LT. We hypothesized that ACLF-3 patients with circulatory and/or respiratory failure before LT would experience worse prognosis after LT. METHODS: We analyzed ACLF-3 patients from the U.S. Scientific Registry of Transplant Recipients, categorized by OF combinations at the time of LT: 1) circulatory failure with other non-respiratory OF, 2) both circulatory and respiratory failures with other OF, 3) all other OF combinations excluding (1) and (2). Cox regression models assessed one-year mortality, and logistic regression examined one-year functional status. RESULTS: Of 5054 ACLF-3 patients, 14 %(728/5054) died within one-year post-LT. The distribution was: 427 patients had circulatory failure with other OF, 1042 had circulatory and respiratory failure with other OF, and 3357 had all other possible combinations of OFs. Patients with both circulatory and respiratory failures experienced higher post-LT mortality than those with circulatory failure plus other OF (one-year Hazard Ratio (HR) for death: 1.32, 95 %CI: 1.08-1.62, p < 0.01). No differences were found between those with circulatory failure plus other OF and those with OF other than circulatory or respiratory. Patients with OF other than circulatory and respiratory failures had better one-year functional status compared to those with circulatory OF (OR for poor functional status: 0.73, 95 %CI: 0.54-0.98, p < 0.01). CONCLUSION: Specific OF combinations affect post-LT outcomes in ACLF-3 patients. Combined respiratory and circulatory failure at LT time was associated with poorer outcomes, regardless of the number of OF.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".