Frailty Measures in Patients Listed for Lung Transplantation.
Bibliographic record
Abstract
<h4>Background</h4>The study aimed to determine whether the addition of cognitive impairment, depression, or both, to the assessment of physical frailty (PF) is associated with the risk of lung transplant (LTX) waitlist mortality.<h4>Methods</h4>Since March 2013, all patients referred for LTX evaluation underwent PF assessment. Cognition was assessed using the Montreal Cognitive Assessment and depression assessed using the Depression in Medical Illness questionnaire. We assessed the association of 4 composite frailty measures: PF ≥3 of 5 = frail, cognitive frailty (CogF ≥ 3 of 6 = frail), depressive frailty (DepF ≥ 3 of 6 = frail), and combined frailty (ComF ≥ 3 of 7 = frail) with waitlist mortality.<h4>Results</h4>The prevalence of PF was 78 (22%), CogF 100 (28%), DepF 105 (29%), and ComF 124 (34%). Waitlist survival in the non-PF group was 94% ± 2% versus 71% ± 7% in the PF group (p < 0.001). Cox proportional hazards regression analysis demonstrated that PF (Adjusted HR, 4.88; 95% CI, 2.06 - 11.56), mild cognitive impairment (Adjusted HR, 3.03; 95% CI, 1.05 - 8.78) and hypoalbuminemia (Adjusted HR, 0.89; 95% CI, 0.82 - 0.97) were independent predictors of waitlist mortality. There was no significant difference in the area under the curve of the 4 frailty measures.<h4>Conclusions</h4>The addition of cognitive function and depression variables to the PF assessment increased the number of patients classified as frail. However, the addition of these variables, does not strengthen the association with LTX waitlist mortality compared to the PF measure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".