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Record W4411431327 · doi:10.1016/j.jhlto.2025.100323

Indications for pediatric lung transplantation in 2025: A new era

2025· article· en· W4411431327 on OpenAlexaffabout
Nicholas Avdimiretz, Don Hayes, Melinda Solomon, Nicolaus Schwerk, Christian Benden

Bibliographic record

VenueJHLT Open · 2025
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsHospital for Sick ChildrenBC Children's Hospital
FundersHealth Resources and Services AdministrationU.S. Department of Health and Human Services
KeywordsLung transplantationMedicineIntensive care medicineLungTransplantationInternal medicine

Abstract

fetched live from OpenAlex

The year 2025 marks an important landmark: almost 40 years since the first pediatric lung transplant (LTX), over 3-5 years since the availability of elexacaftor/tezacaftor/ivacaftor in several countries, and 5-10 years since striking shifts were reported in the diagnoses that accounted for pediatric LTX. We review historic indications for pediatric LTX, highlighting shifts in these over time, and analyze data from the ISHLT International Thoracic Organ Transplant Registry, United Network of Organ Sharing, Canadian Cystic Fibrosis (CF) Registry, and other databases up to the present day. Currently, pediatric CF-related LTX cases are at record lows in many countries. Non-retransplant bronchiolitis obliterans seems to be on the rise as a transplant indication in pediatrics, which is particularly true in the younger age group per ISHLT data. Childhood interstitial lung disease is increasing as an indication, especially in North America. Idiopathic pulmonary arterial hypertension (IPAH) and pulmonary hypertension as a whole now account for record highs as indications for pediatric LTX around the world, with IPAH alone now accounting for nearly 20% of pediatric LTX in the United States, for instance. This information will help guide future international pediatric thoracic transplant consensus guidelines around candidate selection and optimization, placing more emphasis on non-CF considerations.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.311
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.430
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2025
Admission routes2
Has abstractyes

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