An Algorithmic Approach for Assessment of Mediastinal Lesions Using Conventional Transbronchial Needle Aspiration and Endoscopic Ultrasonography in a Single Procedure
Bibliographic record
Abstract
Background . In the era of endobronchial/esophageal ultrasound (EBUS-TBNA/EUS-FNA), many centers forgo conventional transbronchial needle aspiration (C-TBNA) in favour of EBUS-TBNA/EUS-FNA despite no conclusive evidence showing better yields with EBUS-TBNA/EUS-FNA. Objectives . Assess the feasibility of an algorithmic approach for mediastinal sampling beginning with C-TBNA utilizing rapid onsite cytologic evaluation. Methods . Descriptive analysis of 92 consecutive patients referred for adenopathy that underwent C-TBNA and subsequent EBUS-TBNA/EUS-FNA if C-TBNA was negative or nondiagnostic. Results . 92 procedures were analyzed. In 50 (54.3%) of cases, C-TBNA alone was sufficient. EBUS-TBNA was performed after C-TBNA in 27 (29.3%) of cases and EUS-FNA in 33 (35.9%) of cases. The yield was 92.9% for C-TBNA, 92.5% for EBUS-TBNA, and 89.7% for EUS-FNA. There were no statistically significant differences in yields by LN station (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>P</mml:mi><mml:mo>=</mml:mo><mml:mn fontstyle="italic">0.51</mml:mn></mml:math>), the relationship between yield and LN size (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>P</mml:mi><mml:mo>=</mml:mo><mml:mn fontstyle="italic">0.37</mml:mn></mml:math>), or time difference in procedures following the algorithm compared to EBUS/EUS only procedures (33.7 minutes versus 32.4 minutes on average [95% CI for difference: −9.1 to 11.7], <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>P</mml:mi><mml:mo>=</mml:mo><mml:mn fontstyle="italic">0.80</mml:mn></mml:math>). Conclusions . An algorithmic approach to assess the mediastinum using C-TBNA initially is feasible without sacrificing yield or procedure times. C-TBNA was sufficient for diagnosis in 54.3% of cases and can be efficiently taught in an IP training program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".