Development of a minimally invasive pulmonary porcine embolism model via endobronchial ultrasound
Bibliographic record
Abstract
Background: Current massive pulmonary embolism (PE) animal models use central venous access to deliver blood clots, which have features of random clot distribution and potentially fatal hemodynamic compromise. A clinically relevant preclinical model for generating pulmonary emboli in a more controlled fashion would be of value for a variety of research studies, including initial evaluation of novel therapeutic approaches. Endobronchial ultrasound-guided transbronchial needle injection (EBUS-TBNI) is a newly established approach for peri-tracheal/bronchial targets. The purpose of the present work was to establish a minimally invasive PE model in swine via a transbronchial approach. Methods: In anesthetized Yorkshire pigs, a 21-G EBUS-guided transbronchial needle aspiration (EBUS-TBNA) needle was introduced into the pulmonary artery under EBUS guidance. Autologous blood clots were administered into the right and left lower pulmonary arteries sequentially (PE1 and PE2, respectively). Hemodynamic and biochemical responses were evaluated. Results: Ten pigs were evaluated; all 20 blood clots (6.3±1.9 mL) were successfully injected. After injection, mean pulmonary artery pressure (mPAP; mmHg) increased (baseline: 16.6±5.6 vs. PE1: 24.5±7.6, P<0.0001 vs. PE2: 26.9±6.7, P<0.0001), and a positive correlation was observed between clot volume and change in mPAP (PE1: r=0.69, P=0.025; PE1 + PE2: r=0.60, P=0.063). Mean arterial pressure (MAP; mmHg) (baseline: 57.5±5.1 vs. PE1: 59.0±9.1, P=0.918 vs. PE2: 60.9±9.6, P=0.664) remained stable. No complications were observed. Conclusions: EBUS allows minimally invasive, precise, and reliable generation of pulmonary emboli in pigs. This model may serve as an important tool for new PE-related diagnostic and therapeutic research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".