Defining Optimal Settings for Lung Cryobiopsy in End-Stage Pulmonary Disease. A Human, Ex Vivo, Diseased Lung Clinical Trial
Bibliographic record
Abstract
BACKGROUND: To evaluate optimal settings of probe size, freezing time, and distance to the pleura that influence the size and quality of biopsy specimens during transbronchial lung cryobiopsies in ESPD. METHODS: We prospectively recruited 17 patients undergoing lung transplantation. We created a nonperfused ex vivo bronchoscopy setting to perform multiple cryobiopsies with different probe sizes (1.7, 1.9, and 2.4 mm), freezing times (3, 5, 7, 10, 20, 30 seconds), and probe distance from pleura (5, 10, and 20 mm). Alveolated pulmonary parenchyma area≥50% in histology was considered a good quality biopsy, with a minimum procedural artifact. We used logistic regression to identify independent parameters as risk factors for histologic adequacy. RESULTS: A total of 545 cryobiopsies were obtained from 34 explanted lungs after pneumonectomy for lung transplantation. The mean maximum diameter of the specimen achieved with the 1.7 probe was larger (13.5 mm) than those obtained with 1.9 and 2.4 mm probes (11.3 and 10.7 mm, P= 0.07). More pleural macroscopic damage and pleural tissue in histology occurred with the 2.4 mm probe ( P <0.001). There was no difference in the quality of specimens between the different freezing times and the distance from the pleura. CONCLUSIONS: Freezing time and distance from the pleura did not affect the histologic quality for diagnosing ESPD in severely damaged lungs. Smaller cryoprobe size did not negatively affect sample adequacy.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".