Optimization of thrombolytic dose for treatment of pulmonary emboli using endobronchial ultrasound-guided transbronchial needle injection
Bibliographic record
Abstract
Objective Severe pulmonary embolism is often managed with thrombolysis. We sought to determine whether endobronchial ultrasound (EBUS)-guided transbronchial thrombolysis remained effective at lower alteplase doses, with the goal of minimizing potential bleeding risk. Methods Yorkshire pigs were anesthetized and ventilated. Preformed autologous blood clots were administered into bilateral pulmonary arteries via EBUS-guided transbronchial injection. After documenting baseline clot sizes, alteplase was injected into the clots using a 25-gauge transbronchial needle and clot dissolution was monitored over 30 minutes. The study was performed in 2 phases. First, alteplase doses of 5 and 12.5 mg were evaluated. These results informed dose selection for the second phase. Results were compared with 25-mg dose data using EBUS from a previous study. Results In the first phase, 3 clots were evaluated. Distilled water, 5 mg, and 12.5 mg alteplase were administered. The dissolved clot volume (Vdis) and percentage clot volume loss (Rdis) were −10.9, 111.6, and 160.3 mm 3 , and −1.6%, 11.0%, and 59.3%, respectively. In the second phase, alteplase doses of 5, 10, and 15 mg were evaluated in 12 clots across 6 pigs. The Vdis were 247.5 mm 3 (Rdis, 20.1%), 910.8 mm 3 (Rdis, 80.9%), and 798.3 mm 3 (Rdis, 76.0%) for 5, 10, and 15 mg alteplase, respectively. Remakably reduced performance was observed with 5 mg alteplase versus 10 mg (Vdis: P < .001, Rdis: P < .001), and 15 mg (Vdis: P = .004; Rdis: P < .001). No complications were observed. Conclusions Alteplase doses ≥10 mg were optimal for EBUS-guided transbronchial thrombolysis. This technique might represent an effective alternative therapy for central pulmonary embolism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".