The Techniques of Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration
Bibliographic record
Abstract
Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a minimally invasive modality for mediastinal lymph node staging in lung cancer patients as well as for the diagnosis of mediastinal and hilar adenopathy. The high diagnostic yield of EBUS-TBNA for lymph node staging has been shown in systematic reviews and meta-analysis. It has attracted physicians and surgeons as an alternative modality to surgical biopsy for the assessment of patients with enlarged mediastinal and/or hilar lymph nodes. Cell blocks obtained by EBUS-TBNA can be applicable not only for pathologic diagnosis but also for further investigations such as immunohistochemistry and fluorescence in situ hybridization. In addition, samples obtained by EBUS-TBNA can also be used for molecular analysis. Unlike regular bronchoscopy, EBUS-TBNA uses the convex probe EBUS with an ultrasound probe on the tip of a flexible bronchoscope. It is important for the bronchoscopist to fully understand the mediastinal anatomy and be able to correlate it with the ultrasound images for a successful EBUS-TBNA. The dedicated transbronchial needle used for EBUS-TBNA is somewhat different from an ordinary transbronchial biopsy forceps. Training is mandatory for achieving high diagnostic yield without complications. The learning curve of EBUS-TBNA is different from each physician, and continuous training program will be needed for impartiality. This article explains the detailed techniques of EBUS-TBNA to master this innovative procedure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".