The role of endobronchial ultrasound versus mediastinoscopy for non-small cell lung cancer
Bibliographic record
Abstract
This review provides an update on the current role of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) and mediastinoscopy (Med) in assessment of patients with non-small cell lung cancer (NSCLC). Invasive mediastinal lymph node (LN) staging is the major application for both of these techniques. Up until recently, Med was the gold standard for invasive mediastinal LN staging in NSCLC. However, EBUS-TBNA has shown to be equivalent, and in some studies better than Med for invasive staging of lung cancer. EBUS-TBNA offers access to N1 LNs and development of the thin convex probe EBUS (TCP-EBUS) will expand EBUS-TBNA access from the paratracheal region and central airways to more distal parabronchial regions allowing for more extensive N1 LN assessment and sampling more distal lung tumors. EBUS-TBNA is more cost-effective than Med and it is currently recommended as the test of first choice for invasive mediastinal LN staging in lung cancer. Confirmatory Med should be performed selectively in patients with high pretest probability of metastatic disease. Addition of esophageal ultrasound fine needle aspiration (EUS-FNA) may increase diagnostic yield of EBUS-TBNA mediastinal staging. Both Med and EBUS-TBNA can be used in primary lung cancer diagnosis, restaging of the mediastinum following neoadjuvant therapy and in diagnosis of lung cancer recurrence. In the future, a combination of EBUS-TBNA with or without EUS-FNA and Med is most likely going to provide the most optimal invasive assessment of the mediastinum in patients with lung cancer. The decision on test choice and sequence should be made on a case-by-case basis and factoring in local resources and expertise.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".