Diagnostic utility of peripheral endobronchial ultrasound with electromagnetic navigation bronchoscopy in peripheral lung nodules
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: This study aimed to investigate the diagnostic utility of peripheral endobronchial ultrasound (pEBUS) followed by as-needed electromagnetic navigation bronchoscopy (ENB) for sampling peripheral lung nodules. METHODS: The study was a single-arm, prospective cohort study of patients with peripheral lung nodules. Peripheral lung lesion localization was initially performed using a pEBUS probe with guide sheath. If localization failed with pEBUS alone, ENB was used to help identify the lesion. Transbronchial biopsy, bronchial brush, transbronchial needle aspiration and bronchial washings were performed. RESULTS: Sixty patients were enrolled with average lesion size of 27 mm and mean pleural distance of 20 mm. Lesions were found with pEBUS alone in 75% of cases. The addition of ENB improved lesion localization to 93%. However, diagnostic yield for pEBUS alone and pEBUS with ENB were 43% and 50%, respectively. Factors predicting need for ENB use included smaller lesion size and absence of an air bronchus sign on computed tomography. CONCLUSIONS: ENB improves localization of lung lesions after unsuccessful pEBUS but is often not sufficient to ensure confirmation of a specific diagnosis. Technical improvements in sampling methods could improve the diagnostic yield.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".