Monitoring practices of chronic lung allograft dysfunction in pediatric lung transplantation
Bibliographic record
Abstract
INTRODUCTION: Chronic lung allograft dysfunction (CLAD) continues to negatively impact the survival of pediatric lung transplant (LTx) recipients. Current consensus guidelines are adult-focused. We sought to examine CLAD detection and monitoring practices at pediatric LTx programs. METHODS: We conducted a survey among the International Pediatric Lung Transplant Collaborative. Questions consisted of practitioner's experience, LTx program demographics, and querying tests used for CLAD surveillance and detection. Investigations queried included: chest x-ray (CXR), chest computed tomography (CT), lung magnetic resonance imaging (MRI), ventilation/perfusion scanning, conventional pulmonary function testing (PFT), multiple breath washout (MBW), infant/preschool PFT, bronchoalveolar lavage, transbronchial biopsies (TBBx), or other tissue sampling techniques. Preferences for certain modalities over others were questioned based on a five-point Likert scale. RESULTS: Twenty-four of 25 programs responded. Chest CT and CXR are used generally for both CLAD surveillance and detection. No programs use lung MRI clinically, it may have some utility in the future. While all centers use conventional PFT, MBW, and infant/preschool PFT are used in one-fifth and one-third of centers, respectively. While the majority of programs use TBBx, only 41.7% would obtain a diagnosis based on tissue histopathology over noninvasive techniques if CLAD is suspected. Utilization of biomarkers is still limited. CONCLUSIONS: Our results indicate continued use of conventional PFT along with chest CT and less so CXR for CLAD detection and monitoring in the large majority of centers. Infant/preschool PFT and novel methods such as MBW are used in a few centers only. Respondents agreed there is a timely need for pediatric consensus guidelines on CLAD detection and monitoring.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".