Pediatric interventional bronchoscopy: from early limitations to achievable opportunities
Bibliographic record
Abstract
PURPOSE OF REVIEW: The rapid evolution of bronchoscopy equipment and technologies, from the introduction of the 1.1 mm flexible cryoprobe to the use of navigational and robotic bronchoscopy, has afforded unprecedented opportunities for pediatric advanced diagnostic and interventional bronchoscopy. While there is growing interest among pediatric pulmonologists to incorporate these new techniques into their practice, the current pediatric landscape is characterized by few practicing interventional bronchoscopists, scant published literature, and no formal training programs. RECENT FINDINGS: While the majority of the published literature consists of case reports and small case series, the increased application of new techniques is starting to yield larger and more structured studies that will be able to provide more objective commentary on the proposed indications, safety, and efficacy of such techniques in the pediatric population. SUMMARY: For many decades, progress in pediatric advanced diagnostic and interventional bronchoscopy was slow and deliberate, limited by the lack of appropriately sized equipment and experienced interventional bronchoscopists. The current opportunities afforded require equal, or perhaps even more, vigilance as pediatric pulmonologists employ new equipment and technologies and define new practices and standards of care. Importantly, this review is meant to serve as a general conspectus of pediatric advanced diagnostic and interventional bronchoscopy and not a consensus guideline for the performance of advanced or even routine bronchoscopy in the pediatric population. For technical standards of pediatric bronchoscopy, refer to existing guidelines [1,2] .
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".