An update on frailty in lung transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Frailty is prevalent in lung transplant candidates, and recent studies have demonstrated associations with increased mortality before and after transplantation. This review highlights important findings on the trajectory of frailty throughout the lung transplant process and provides valuable insight into frailty and some of its modifiable elements. RECENT FINDINGS: There have been several frailty indices used in lung transplantation, specifically the Frailty Phenotype, Short Physical Performance Battery (SPPB), and Cumulative Deficits. The two most commonly used measures - Frailty Phenotype and SPPB - reflect physical frailty and have been associated with increased morbidity and mortality pre and post-transplantation. However, there is emerging evidence that physical elements of frailty are reversible with rehabilitation before and after transplantation with improvement in frailty by 6 months after transplantation. The associations of frailty with physical activity levels, exercise capacity, and inflammation are discussed. SUMMARY: Frailty is prevalent before transplant, but physical frailty is modifiable with rehabilitation and transplantation. Thus, physical frailty should not be an absolute contraindication to lung transplantation, but efforts should focus on elements of frailty that are potentially modifiable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".