Diagnostic effectiveness and safety of robotic-assisted bronchoscopy for subsolid pulmonary nodules: A multicenter prospective observational study
Bibliographic record
Abstract
OBJECTIVE: To analyze the effectiveness and safety of robotic-assisted bronchoscopy (RAB) for subsolid nodules, including semisolid nodules (SSNs) and ground-glass nodules (GGNs). METHODS: This is a subset analysis of a multicenter, prospective observational study that investigated patients with subsolid nodules undergoing RAB. Effectiveness was defined by reach, measured by radial endobronchial ultrasound (rEBUS) confirmation and median distance from bronchoscope tip to the lesion prior to biopsy, and access, measured by sensitivity for malignancy and diagnostic yield (DY). Adverse events were recorded to assess safety. RESULTS: Of 679 patients analyzed, 91 (13.4%) had subsolid nodules, including 78 with SSNs and 13 with pure GGNs. The median subsolid nodule size was 18.5 mm, 61.5% were in the upper lobes, and 89.0% were in the outer two-thirds of the lung. The malignancy rate was 45.1%, predominantly adenocarcinoma. rEBUS localization rates were 81.4% for SSNs and 61.5% for GGNs, with median distance from the bronchoscope tip of 22.0 mm and 19.5 mm, respectively. Sensitivity for malignancy was 77.8% for SSNs and 75.0% for GGNs, while the strict DY was 50.0% and 61.5%, respectively. Nondiagnostic results occurred in 31 subsolid nodules (34.1%) at the index biopsy. The incidence of pneumothorax was 4.4% and that of pneumothorax necessitating a chest tube was 2.2% in the subsolid nodule cohort. No bleeding, respiratory complications, or mortalities were reported in the subsolid nodule cohort during the 7-day follow-up period. CONCLUSIONS: Our findings confirm that RAB is safe and effective for diagnosing subsolid nodules.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".