Risk Factors for Death of Patients with Cystic Fibrosis Awaiting Lung Transplantation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it