The Societal Impact of Single Versus Bilateral Lung Transplantation for Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
RATIONALE: Bilateral lung transplantation (BLT) improves survival compared with single lung transplantation (SLT) for some individuals with chronic obstructive pulmonary disease (COPD). However, it is unclear which strategy optimally uses this scarce societal resource. OBJECTIVES: To compare the effect of SLT versus BLT strategies for COPD on waitlist outcomes among the broader population of patients listed for lung transplantation. METHODS: We developed a Markov model to simulate the transplant waitlist using transplant registry data to define waitlist size, donor frequency, the risk of death awaiting transplant, and disease- and procedure-specific post-transplant survival. We then applied this model to 1,000 simulated patients and compared the number of patients under each strategy who received a transplant, the number who died before transplantation, and total post-transplant survival. MEASUREMENTS AND MAIN RESULTS: Under baseline assumptions, the SLT strategy resulted in more patients transplanted (809 vs. 758) and fewer waitlist deaths (157 vs. 199). The strategies produced similar total post-transplant survival (SLT = 4,586 yr vs. BLT = 4,577 yr). In sensitivity analyses, SLT always maximized the number of patients transplanted. The strategy that maximized post-transplant survival depended on the relative survival benefit of BLT versus SLT among patients with COPD, donor interval, and waitlist size. CONCLUSIONS: In most circumstances, a policy of SLT for COPD improves access to organs for other potential recipients without significant reductions in total post-transplant survival. However, there may be substantial geographic variations in the effect of such a policy on the balance between these outcomes.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".