Translational Research in Pediatrics III: Bronchoalveolar Lavage
Bibliographic record
Abstract
The role of flexible bronchoscopy and bronchoalveolar lavage (BAL) for the care of children with airway and pulmonary diseases is well established, with collected BAL fluid most often used clinically for microbiologic pathogen identification and cellular analyses. More recently, powerful analytic research methods have been used to investigate BAL samples to better understand the pathophysiological basis of pediatric respiratory disease. Investigations have focused on the cellular components contained in BAL fluid, such as macrophages, lymphocytes, neutrophils, eosinophils, and mast cells, as well as the noncellular components such as serum molecules, inflammatory proteins, and surfactant. Molecular techniques are frequently used to investigate BAL fluid for the presence of infectious pathologies and for cellular gene expression. Recent advances in proteomics allow identification of multiple protein expression patterns linked to specific respiratory diseases, whereas newer analytic techniques allow for investigations on surfactant quantification and function. These translational research studies on BAL fluid have aided our understanding of pulmonary inflammation and the injury/repair responses in children. We review the ethics and practices for the execution of BAL in children for translational research purposes, with an emphasis on the optimal handling and processing of BAL samples.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".