Frailty predicts outcomes in cystic fibrosis patients listed for lung transplantation
Bibliographic record
Abstract
BACKGROUND: Survival predictors are not established for cystic fibrosis (CF) patients listed for lung transplantation (LT). Using the deficit accumulation approach, we developed a CF-specific frailty index (FI) to allow risk stratification for adverse waitlist and post-LT outcomes. METHODS: We studied adult CF patients listed for LT in the Toronto LT Program (development cohort 2005-2015) and the Swiss LT centres (validation cohort 2008-2017). Comorbidities, treatment, laboratory results and social support at listing were utilized to develop a lung disease severity index (LI deficits, d = 18), a frailty index (FI, d = 66) and a lifestyle/social vulnerability index (LSVI, d = 10). We evaluated associations of the indices with worsening waitlist status, hospital and ICU length of stay, survival and graft failure. RESULTS: We studied 188 (Toronto cohort, 176 [94%] transplanted) and 94 (Swiss cohort, 89 [95%] transplanted) patients. The median waitlist times were 69 and 284 days, respectively. The median follow-up post-transplant was 5.3 and 4.7 years. At listing, 44.7% of patients were frail (FI ≥ 0.25) in the Toronto and 21.3% in the Swiss cohort. The FI was significantly associated with all studied outcomes in the Toronto cohort (FI and post-LT mortality, multivariable HR 1.74 [95%CI:1.24-2.45] per 0.1 point of the FI). In the Swiss cohort, the FI was associated with worsening waitlist status, post-LT mortality and graft failure. CONCLUSIONS: In CF patients listed for LT, FI risk stratification was significantly associated with waitlist and post-LT outcomes. Studying frailty in young populations with advanced disease can provide insights on how frailty and deficit accumulation impacts survival.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".