CT-Guided vs. Navigational Bronchoscopic Biopsies for Solitary Pulmonary Nodules: A Single-Institution Retrospective Comparison
Bibliographic record
Abstract
Objective: Lung cancer is the second most common cause of death by cancer. Multiple modalities can be used to obtain a tissue sample from a pulmonary nodule. We aimed to compare the yield and adverse events related to transthoracic needle aspiration (TTNA) and Electromagnetic Navigation Biopsy (ENB) at our institution. Methods: This was a single-center retrospective study in which all patients referred for evaluation of a pulmonary lesion over 5 years (1 January 2013 to 31 December 2018) were identified. Our primary outcome was to compare the accuracy of TTNA to that of ENB in establishing the diagnosis of pulmonary lesions. Secondary outcomes included the evaluation of the adverse events and the sensitivity, specificity, positive, and negative predictive value of each modality. Results: A total of 1006 patients were analyzed. The mean age of patients in the TTNA and the ENB group was 67.2 ± 11.2 years and 68.3 ± 9.2 years respectively. Local anesthesia was predominantly used for TTNA and moderate sedation was more commonly used in the ENB group. We found ENB to have an accuracy of 57.1%, with a sensitivity of 40.0%, a specificity of 100.0%, a positive predictive value of 100.0%, and a negative predictive value of 40.0%. As for the TTNA, the accuracy was 75.9%, with a sensitivity of 77.5%, a specificity of 61.5%, a positive predictive value of 95.0%, and a negative predictive value of 22.5%. The rate of clinically significant complications was higher in the TTNA group (8.2%) as compared to the ENB group (4.7%) with a p-value < 0.001. Conclusion: TTNA was superior to ENB-guided biopsy for the diagnostic evaluation of lung nodules. However, the complication rate was much higher in the TTNA group as compared to the ENB group.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".