Transbronchial biopsies in lung cancer: Retrospective review of 116 patients
Bibliographic record
Abstract
Background: Bronchoscopy with transbronchial biopsy (TBB) is an interesting tool in the investigation of lung cancer. We aimed to describe the yield and safety of TBB in the evaluation of pulmonary malignancy. Methods: We retrospectively reviewed the data of 116 TBB performed to assess lung lesions executed between January 2013 and December 2013 at a tertiary center. We evaluated these procedures in terms of diagnostic yield for neoplasia and complications. Results: The mean age of the patients was 66 years old (range 26-88), with 55 male (48.9%). The mean number of biopsies was 4.28 (±1.46). The diagnostic yield from TBB alone for lung cancer was 37.9% (44/116), while when combined with other sampling procedures (such as bronchial biopsy, bronchoalveolar lavage or brush), diagnostic yield was 43.1% (50/116). When linear endobronchial ultrasound (EBUS) was performed during the same procedure, diagnostic yield further increased to 58.6% (68/116). A higher number of biopsies improved the diagnostic yield (72.7% with > 5 biopsies vs 36.7% with 3-5 biopsies vs 0% with 1-2 biopsies; p= 0.015). Diagnostic yield was higher when lesions measured ≥ 30 mm (46.3%; 31/67) compared with nodules <30 mm in diameter (25.6%; 10/39) [p=0.041]. Adjuncts such as radial EBUS (8/116), electromagnetic navigation (19/116), fluoroscopy (17/116) and cryoprobe (4/116) were used in 29 TBB (25%), with no significant effect on diagnostic yield (p=0.659). Only one patient suffered from a small pneumothorax (0.9 %) that did not require chest drainage. Conclusions: Efficacy of TBB for the diagnosis of lung cancer is optimized when performed with linear EBUS, > 5 biopsies and larger lung lesion.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".