Mediastinal Staging with Endobronchial Ultrasound in Early-Stage Non—Small Cell Lung Cancer: Is It Necessary?
Bibliographic record
Abstract
Herein we examine the need for minimally invasive mediastinal staging for patients with early-stage non-small cell lung cancer (NSCLC) using endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). Early NSCLC, stages 1 and 2, has a 5-year survival rate between 53 and 92%, whereas stages 3 and 4 have a 5-year survival of 36% and below. With more favorable outcomes in earlier stages, greater emphasis has been placed on identifying lung cancer earlier in its disease process. Accurate staging is crucial as it dictates both prognosis and therapy. Inaccurate staging can adversely impact surgical candidacy (if falsely "over-staged") or lead to inadequate treatment (if "under-staged"). Clinical staging utilizes noninvasive methods to evaluate the anatomic extent of disease; however, it remains controversial whether mediastinal staging of early NSCLC with radiological exams alone is sufficient. EBUS-TBNA has altered the landscape of invasive mediastinal staging and is a crucial component to improving confidence in lung cancer staging, specifically in early NSCLC. Radiographic occult lymph node metastasis identified upon review of surgical resection specimens of early NSCLC may support the argument to perform EBUS-TBNA in all cases of early-stage disease. Other data suggest that EBUS-TBNA could be spared in cases of peripheral cT1aN0 and cT1bN0 for which surgical resection with lymph node dissection is planned. By reviewing reported EBUS-TBNA outcomes in patients with early NSCLC, we aim to emphasize the necessity of staging with EBUS in this population.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".