The fundamentals of sex‐based disparity in liver transplantation: Understanding can lead to change
Bibliographic record
Abstract
Liver transplantation (LT) is the definitive treatment for end-stage liver disease. Unfortunately, women are disadvantaged at every stage of the LT process. We conducted a literature review to increase the understanding of this disparity. Hormonal differences, psychological factors, and Model for End-Stage Liver Disease (MELD) score inequalities are some pretransplantation factors that contribute to this disparity. In the posttransplantation setting, women have differing risk than men in most major outcomes (perioperative complications, rejection, long-term renal dysfunction, and malignancy) and assessing the two groups together is disadvantageous. Herein, we propose interventions including standardized criteria for LT referral, using an alternate MELD, education for support of women, and motivating women to seek living donors. Understanding sex-based differences will allow us to improve access, tailor management, and improve overall outcomes for all patients, particularly women.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".