Addressing sex-based disparities in solid organ transplantation in the United States – a conference report
Bibliographic record
Abstract
Solid organ transplantation provides the best treatment for end-stage organ failure, but significant sex-based disparities in transplant access exist. On June 25, 2021, a virtual multidisciplinary conference was convened to address sex-based disparities in transplantation. Common themes contributing to sex-based disparities were noted across kidney, liver, heart, and lung transplantation, specifically the existence of barriers to referral and wait listing for women, the pitfalls of using serum creatinine, the issue of donor/recipient size mismatch, approaches to frailty and a higher prevalence of allosensitization among women. In addition, actionable solutions to improve access to transplantation were identified, including alterations to the current allocation system, surgical interventions on donor organs, and the incorporation of objective frailty metrics into the evaluation process. Key knowledge gaps and high-priority areas for future investigation were also discussed.
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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 | 0.029 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".