Bibliographic record
Abstract
PURPOSE OF REVIEW: Advances in diagnostic bronchoscopy seek to optimize diagnostic yield while maintaining the safety profile of conventional bronchoscopy. Despite rapid technological progress, inconsistent outcome definitions have hindered pooling of data across studies and comparisons across technologies. This review aims to clarify how a standardized definition of diagnostic yield can enhance comparability and clinical interpretation in advanced diagnostic bronchoscopy. RECENT FINDINGS: The recent ATS/CHEST consensus statement provides a rigorous framework for defining and reporting diagnostic yield, enabling meaningful cross-study comparisons. Beyond definitions of diagnostic outcome measures, optimization of tissue acquisition and processing through close collaboration between bronchoscopists and pathologists is critical. Current evidence supports the use of coordinated sampling strategies to secure sufficient, high-quality material for molecular testing, particularly for large-panel next-generation sequencing (NGS). Advances in EBUS-TBNA, cryobiopsy, and robotic bronchoscopy have improved sample yield and quality for genomic profiling. In parallel, liquid biopsy using circulating tumor DNA provides a minimally invasive adjunct, particularly valuable when tissue is limited, exhausted, or longitudinal monitoring is required; however, its sensitivity remains constrained in low-shedding diseases. SUMMARY: Adherence to a strict definition of diagnostic yield, combined with optimized sampling and integrated molecular testing, ensures that technological innovation in bronchoscopy translates into clinically meaningful, precise, and patient-centered diagnosis of lung cancer.
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 | 0.042 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".