The future of surgical lung biopsy: moving from the operating room to the bronchoscopy suite
Bibliographic record
Abstract
Abstract: Assessment of patients with suspected interstitial lung disease includes a complete work-up consisting of history and physical examination, laboratory studies, lung function testing, high-resolution CT scan, bronchoscopy with bronchoalveolar lavage, and cardiology workup. Presently, definitive diagnosis of interstitial lung disease is even more important because of newly available therapeutics. The high risk/benefit ratio perceived in the literature for surgical lung biopsy motivates research for new biopsy techniques that ideally would have the same or better diagnostic yield as surgical lung biopsy with less morbidity, mortality and cost. If endoscopic transbronchial lung cryobiopsy can be shown to be associated with a low morbidity rate and high accuracy, we may be able to obtain a definitive histological diagnosis in a great majority of interstitial lung disease cases and forego complex, inefficient and inaccurate methods of disease diagnosis. Several groups have published prospective and retrospective series on endoscopic transbronchial lung cryobiopsy for interstitial lung disease with promising results and diagnostic yields up to 83% (range, 74–91%). The main complications associated with endoscopic transbronchial lung cryobiopsy are pneumothoraces and bleeding. The optimal techniques and specific settings for endoscopic transbronchial lung cryobiopsy is currently being investigated. In this paper, we present a review of the literature for endoscopic transbronchial lung cryobiopsy and future perspectives.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".