The changing landscape of pediatric lung transplantation
Bibliographic record
Abstract
There has been a shift over decades in the diagnostic indications for lung transplantation in children; in particular, there has been a reduction in the proportion of pediatric cystic fibrosis (CF) patients undergoing lung transplantation early in life, and more transplants occurring in other diagnostic groups. Here, we examine trends in pediatric lung transplantation with regards to indications by analyzing data from the United Network of Organ Sharing, the International Society for Heart and Lung Transplantation Thoracic Transplant Registry, and other sources. Over the past two years, there has been a precipitous decline in both the number of transplants due to CF and the proportion of CF cases relative to the total number of transplants, likely not solely due to the COVID-19 pandemic. In 2020, primary pulmonary arterial hypertension for the first-time surpassed CF as main indication for pediatric lung transplantation in the United States, a finding that is also reflected in international data. We discuss the effect of novel CFTR modulator therapies as a major factor leading to this shifting landscape. Based on our trending, pulmonary hypertension-related diagnoses and pediatric interstitial lung diseases are rising indications, for which we suggest adjustments of consensus guidelines around candidate selection criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".