Report from the American Society of Transplantation on frailty in solid organ transplantation
Bibliographic record
Abstract
A consensus conference on frailty in kidney, liver, heart, and lung transplantation sponsored by the American Society of Transplantation (AST) and endorsed by the American Society of Nephrology (ASN), the American Society of Transplant Surgeons (ASTS), and the Canadian Society of Transplantation (CST) took place on February 11, 2018 in Phoenix, Arizona. Input from the transplant community through scheduled conference calls enabled wide discussion of current concepts in frailty, exploration of best practices for frailty risk assessment of transplant candidates and for management after transplant, and development of ideas for future research. A current understanding of frailty was compiled by each of the solid organ groups and is presented in this paper. Frailty is a common entity in patients with end-stage organ disease who are awaiting organ transplantation, and affects mortality on the waitlist and in the posttransplant period. The optimal methods by which frailty should be measured in each organ group are yet to be determined, but studies are underway. Interventions to reverse frailty vary among organ groups and appear promising. This conference achieved its intent to highlight the importance of frailty in organ transplantation and to plant the seeds for further discussion and research in this field.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| 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".