Evaluation of Pulmonary Nodules Using the Spyglass Direct Visualization System Combined With Radial Endobronchial Ultrasound: A Clinical Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Sampling of peripheral pulmonary nodules with radial endobronchial ultrasound (p-EBUS) increases diagnostic yield of bronchoscopy. However, diagnostic yield is influenced by numerous factors. OBJECTIVE: We evaluated the use of SpyGlass, a one millimeter diameter optic fiber, to obtain images of the distal mucosa and of pulmonary lesions detected with p-EBUS to determine if visual aspect of the distal mucosa was predictive of diagnosis. METHODS: We prospectively recruited subjects investigated for peripheral nodules. Bronchoscopy was performed and p-EBUS was used to locate the lesion through a guide sheath. The Spyglass fiber was introduced in the sheath to obtain images of the distal bronchial mucosa. Tissue sampling was subsequently done. RESULTS: Fifteen patients were enrolled in the study. A final diagnosis of malignancy was confirmed in 80%. All lesions could be located using p-EBUS (100%). Diagnostic sensitivity for p-EBUS was 58.3%. Distal mucosa could be imaged with SpyGlass in 14/15 patients (93.3%). Mucosal appearance was described as abnormal in 7 out of the 15 subjects. Mean SpyGlass procedure time was 6.5 minutes. No direct complication was reported. CONCLUSION: Spyglass can be used in combination with p-EBUS to obtain images of the distal bronchial mucosa and peripheral pulmonary nodules. More patients will be needed to confirm whether mucosal appearance can be predictive of malignancy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".