Frailty and aging-associated syndromes in lung transplant candidates and recipients
Bibliographic record
Abstract
Many lung transplant candidates and recipients are older and frailer compared to previous eras. Older patients are at increased risk for pre- and posttransplant mortality, but this risk is not explained by numerical age alone. This manuscript represents the product of the American Society of Transplantation (AST) conference on frailty. Experts in the field reviewed the latest published research on assessment of elderly and frail lung transplant candidates. Physical frailty, often defined as slowness, weakness, low physical activity, shrinking, and exhaustion, and frailty evaluation is an important tool for evaluation of age-associated dysfunction. Another approach is assessment by cumulative deficits, and both types of frailty are common in lung transplant candidates. Frailty is associated with death or delisting before transplant, and may be associated with posttransplant mortality. Sarcopenia, cognitive dysfunction, depression, and nutrition are other important components for patient evaluation. Aging-associated inflammation, telomere dysfunction, and adaptive immune system senescence may also contribute to frailty. Developing tools for frailty assessment and interventions holds promise for improving patient outcomes before and after lung transplantation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".