Advances in Invasive Diagnostics in Lung Cancer
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer incidence and mortality worldwide. Pulmonologists play a central role in the timely, guideline-concordant diagnosis and staging of lung cancer. Minimally invasive procedures must also provide sufficient tissue for advanced molecular testing, particularly in light of the evolving landscape of lung cancer treatment. Advanced diagnostic bronchoscopy has developed at an accelerated pace over the last two decades, with a widening array of tools and technologies. Minimally invasive diagnostic sampling is typically guided by the suspected stage of disease. Linear endobronchial ultrasound has an established role in the diagnosis and staging of lung cancer. Novel technologies targeting the lung periphery aim to overcome the challenge of successfully reaching peripheral lung lesions and bridge the diagnostic gap by acquiring adequate samples. Advanced imaging modalities are combined with electromagnetic navigation, ultrathin bronchoscopy, and robotic-assisted bronchoscopy platforms. Herein, we review recent advances in invasive diagnostics in lung cancer, with a focus on interventional pulmonary procedures. The importance of strictly defined diagnostic outcomes in the advanced bronchoscopy literature is highlighted, as is the ongoing need for comparative effectiveness studies.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".