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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".