Lung Transplantation for Pulmonary Hypertension and Strategies to Bridge to Transplant
Bibliographic record
Abstract
Abstract Despite an expanded armamentarium of medical therapies, pulmonary arterial hypertension (PAH) remains a progressive disease associated with significant morbidity and premature mortality. Lung transplantation (LT) is the last safety net for patients failing medical therapy, providing excellent overall long-term survival and health-related quality-of-life outcomes in line with more common parenchymal lung disease indications. Waitlist mortality remains disproportionally elevated, however, reflecting an inability of the lung allocation score to completely capture PAH disease severity, and a propensity for PAH to deteriorate rapidly without warning, even in patients who appear externally “well.” Early referral to a LT center can mitigate these risks and facilitate rapid listing if necessary. Several bridging therapies are available to support severely unwell patients to LT, such as extracorporeal life support (ECLS) and atrial septostomy. Unique perioperative considerations include higher rates of primary graft dysfunction and dynamic right ventricular outflow obstruction which may, at least in part, reflect rapid afterload reduction in the face of a conditioned right ventricle. Extending ECLS into the perioperative period may ameliorate these risks by allowing more gradual adaptation of both ventricles to their new loading conditions. Chronic lung allograft dysfunction, particularly bronchiolitis obliterans syndrome, remains a major cause of long-term morbidity and mortality, and complications from corticosteroid and immunosuppressive therapy are common. Nevertheless, the morbidity, mortality, and burden of disease management after LT continue to improve and compare favorably to that of refractory PAH in carefully selected patients.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".