Transplantation, bridging, and support technologies in pulmonary hypertension
Bibliographic record
Abstract
Despite the progress made in medical therapies for treating pulmonary hypertension (PH), a subset of patients remain susceptible to developing a maladaptive right ventricular phenotype. The effective management of end-stage PH presents substantial challenges, necessitating a multidisciplinary approach and early identification of patients prone to acute decompensation. Identifying potential transplant candidates and assessing the feasibility of such a procedure are pivotal tasks that should be undertaken early in the treatment algorithm. Inclusion on the transplant list is contingent upon a comprehensive risk assessment, also considering the specific type of PH and various factors affecting waiting times, all of which should inform the decision-making process. While bilateral lung transplantation is the preferred option, it demands expert intra- and post-operative management to mitigate the heightened risks of pulmonary oedema and primary graft dysfunction in PH patients. Despite the availability of risk assessment tools, the occurrence of acute PH decompensation episodes can be unpredictable, potentially leading to refractory right ventricular failure even with optimal medical intervention, necessitating the use of rescue therapies. Advancements in right ventricular assist techniques and adjustments to graft allocation protocols for the most critically ill patients have significantly enhanced the survival in intensive care, affording the opportunity to endure while awaiting an urgent transplant. Given the breadth of therapeutic options available, specialised centres capable of delivering comprehensive care have become indispensable for optimising patient outcomes. These centres are instrumental in providing holistic support and management tailored to the complex needs of PH patients, ultimately enhancing their chances of a successful transplant and improved long-term prognosis.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".