Yield of EBUS-TBNA for the diagnosis of sarcoidosis: impact of operator and cytopathologist experience
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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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 | 0.008 | 0.059 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".