EBUS-TBNA in a tertiary care center: Real life experience and quality assessment
Bibliographic record
Abstract
INTRODUCTION Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has become the standard minimally invasive modality for sampling mediastinal lymph nodes. In this study, we evaluate our performance with the technique and propose a methodology that can be used in other center. METHOD We included all EBUS-TBNA procedures performed in 2011 in our service. We proceeded to a thorough analysis of each case from a pragmatic perspective to evaluate the regional node sampling yield and our ability to conclude the investigation by achieving diagnosis and staging, avoiding unnecessary surgery and allowing direction for management. RESULTS EBUS-TBNA was performed in 53 consecutive patients. The indication was suspicion of malignant disease in 46 patients (87%) and benign in 7 patients (13%). In total, 117 nodes were sampled. The overall sensitivity ranged from 76 to 91% and the negative predictive value ranged from 78 to 92%. The median size of nodes was 15 mm. If malignant cells were discovered, histological precision was obtained in 68%. EBUS-TBNA was clinically sufficient in 43 of the 53 patients (diagnostic yield = 81%) and prevented 18 out of 27 patients (67%) from undergoing surgery. No major complications were reported. CONCLUSION The attained sensitivity and negative predicitive value are similar to what has been published. Thorough analysis of patient cases is key in improving performance with this technique, and we encourage all centers to analyze their practice periodically in order to maintain quality standards. Finally, in our establishment, EBUS-TBNA offers clinicians an additional step to simplify healthcare and probably reduce the economic burden of such investigations.
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 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".