Yield of EBUS-TBNA for the diagnosis of sarcoidosis: impact of operator and cytopathologist experience
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Bibliographic record
Abstract
BACKGROUND: Studies have reported a high diagnostic yield of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) for the diagnosis of sarcoidosis. We sought to review the yield of EBUS-TBNA for the diagnosis of sarcoidosis at our institution over time, and examine factors that may influence this yield. METHODS: Patients who underwent EBUS-TBNA for suspected sarcoidosis between December 2008 and November 2011 were identified. EBUS was performed without rapid on-site evaluation (ROSE) of samples. The final diagnosis was based on the results of all invasive diagnostic procedures and/or clinical follow-up. Logistic regression analysis was used to examine the effect of various factors on yield. RESULTS: 43 patients underwent 45 EBUS-TBNA procedures for suspected sarcoidosis. A total of 115 lymph nodes were sampled. The 21 G needle was used in 51% of procedures. The mean number of lymph node stations sampled was 2.6 (SD 0.7) and the mean number of needle passes per procedure was 7.8 (SD 2.0). Non-necrotising granulomatous inflammation was detected in EBUS-TBNA samples from 34/45 (76%) procedures. The overall diagnostic yield increased to 36/45 (80%) following a cytopathology review for this study. Needle gauge, number of lymph node stations sampled and number of needle passes were not associated with diagnostic yield. The yield of EBUS-TBNA increased significantly after the first 15 procedures performed for suspected sarcoidosis; the 2 additional cases diagnosed after the cytopathology review were part of this early experience. CONCLUSIONS: EBUS-TBNA is a valuable technique for the diagnosis of sarcoidosis when performed without ROSE. The yield of the procedure improved significantly over time, based on operator and cytopathologist experience.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it