Efficient utilization of EBUS-TBNA samples for both diagnosis and molecular analyses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The assessment of an increasing number of molecular markers is becoming a standard requirement from endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) specimens. However, it is unclear how many needle passes should be performed and the amount of lung cancer cells that should be sent for molecular analyses. The objective of this study was to determine if it is feasible to divide the material obtained by EBUS-TBNA to allow for molecular analysis without compromising the accuracy of mediastinal staging. OBJECTIVE: We aimed to determine if dividing EBUS-TBNA specimens has a negative impact on either histopathological diagnosis or molecular analysis. METHODS: EBUS-TBNA was performed in 249 enlarged lymph nodes. Negative or ambiguous histopathological results were confirmed by surgical means and clinical follow-up over 6 months. The tissue obtained by EBUS-TBNA was placed onto a glass slide and divided for histopathological workup and molecular analysis. The number of passes was recorded. Both the accuracy of the mediastinal lymph node staging and the applicability of the sample division for molecular analysis were assessed. RESULTS: Each lymph node was punctured an average of 3.18 times and division of the obtained material for diagnosis and molecular analysis was feasible in all cases. The sensitivity and accuracy of the mediastinal lymph node staging were 96.6% and 97.6%, respectively. A cytokeratin (CK)-19-mRNA concentration-based molecular test was feasible in 74.1% of cases. CONCLUSION: Dividing EBUS-TBNA samples for both histopathological diagnosis and molecular testing is feasible and does not compromise the accuracy of mediastinal staging. This method may be an alternative to taking additional needle passes for molecular analyses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it