Abstracts
Bibliographic record
Abstract
MELD predicts waitlist mortality; consequently, liver waitlist candidates with higher MELD get higher allocation priority. However, this disease severity-based allocation system is controversial due to concern that the sickest patients might not benefit from liver transplantation (LT). We conducted a national study to quantify survival benefit of LT across the range of MELD, stratifying by organ quality (DRI). Methods: We identified 66,569 adult liver-only candidates/recipients 2006-2015 from SRTR. Patients with status 1 or exception points were excluded. We compared deceased donor recipients to patients waiting for LT, using generalized gamma parametric survival analysis and adjusting for MELD, age, gender, race, blood type, liver diseases, malignancy, and insurance. Predicted change in survival and life years gained due to LT (truncating at 10y; Figure Results: LT was associated with increased survival at MELD11 for low and medium-DRI livers and MELD16 for high-DRI livers (time ratio (TR)>1 indicating longer survival after transplant). This association was stronger in higher MELD strata across all DRI categories, with greatest relative survival at MELD=40 (Table Life years gained due to LT increased for patients with higher MELD, and remained roughly constant for patients with MELD>25 (Figure Patients with MELD>25 gained about 7, 5, and 4 life years from livers with low, medium, and high DRI, respectively. Conclusions: Higher MELD correlates directly with increased relative survivalacross the range of MELD. However, when survival benefit is calculated as life years gained, all patients with MELD>25 benefit equally. The priority between relative scale and absolute (life years saved) scale becomes policy decision, not a mathematical one. Table 1: Time ratio: >1 = longer life post-LT (bold); <1 = shorter life post-LT (underline) MELD Low DRI (1.20) Medium DRI (1.20-1.57) High DRI (1.57) 6-10 0.57 1.37 3.28 0.12 0.27 0.59 0.14 0.24 0.40 11-15 1.83 2.30 2.88 1.08 1.39 1.78 0.59 0.74 0.93 16-20 3.62 4.10 4.64 2.06 2.41 2.81 1.47 1.72 2.01 21-25 7.26 8.13 9.10 4.30 4.99 5.79 2.45 2.88 3.39 26-30 24.05 27.24 30.86 10.66 12.60 14.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.731 | 0.522 |
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".