Risk Factors for Death of Patients with Cystic Fibrosis Awaiting Lung Transplantation
Bibliographic record
Abstract
RATIONALE: The optimal timing for listing of cystic fibrosis patients for lung transplantation is controversial. OBJECTIVES: We conducted a retrospective cohort study of 343 patients listed for lung transplantation at four academic medical centers to identify risk factors for death while awaiting transplantation. METHODS: Data on possible risk factors were abstracted from medical records. MEASUREMENTS: Time to death, patient demographic characteristics, and risk factors for death while awaiting transplantation were assessed. Univariate and multivariate survival analyses were performed using Cox regression. RESULTS: By univariate analyses, FEV1 < or = 30% predicted (HR, 3.8; 95% CI, 2.0-7.5), Pa(CO2) > or = 50 mm Hg (HR, 1.85; 95% CI, 1.1-3.0), and shorter height (HR, 1.8; 95% CI, 1.1-3.0) were associated with a higher risk of death. Referral from an accredited cystic fibrosis center was associated with a lower risk (HR, 0.53; 95% CI, 0.30-0.92). The final multivariate model included referral from an accredited cystic fibrosis center (HR, 0.5; 95% CI, 0.3-1.0) and listing year after 1996 (HR, 0.4; 95% CI, 0.2-0.7); both were associated with a lower risk of death. FEV1 < or = 30% predicted (HR, 6.8; 95% CI, 2.4-19.3), Pa(CO2) > or = 50 mm Hg (HR, 6.9; 95% CI, 1.5-32.1), and use of a nutritional intervention (HR, 2.3; 95% CI, 1.3-4.1) were associated with increased risk. Patients with FEV1 > 30% predicted had a higher risk of death only when their Pa(CO2) was > or = 50 mm Hg (HR, 7.0; 95% CI, 1.5-32), while the increased risk of death with FEV1 < or = 30% was not further influenced by the presence of hypercapnia. CONCLUSIONS: We identified risk factors for waiting list mortality that could impact on transplant listing and allocation guidelines.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".