Utilization of POCUS in Acute Pulmonary Embolism with Hemodynamic Instability: A Case Presentation
Bibliographic record
Abstract
Introduction: A pulmonary embolism (PE) is a life-threatening condition requiring rapid identification and treatment. However, the non-specific symptoms associated with the acute onset of a PE make clinical diagnosis difficult. Point of care ultrasound (POCUS) is a readily available and evolving technique that allows for rapid identification of a PE. Case Presentation: 57-year-old patient presented to the Emergency Department (ED) hemodynamically unstable following an acute onset of shortness of breath and syncope. Ultrasonography revealed right ventricle (RV) dysfunction, abnormal tricuspid annular plane systolic excursion (TAPSE), and intravascular thrombosis, indicating a PE. Subsequently, the computed tomography pulmonary angiogram (CTPA) showed large bilateral pulmonary emboli and the patient received tissue plasminogen activator (TPA). Discussion: The dependence of EDs on CTPA to rule in a PE before initiating thrombolytics may delay life-saving treatment. This case demonstrates the valuable addition of POCUS to the diagnostic protocol for PEs and reduces the waiting period in hemodynamically unstable patients to initiate empiric reperfusion therapy. Conclusion: This case report demonstrates the benefits of POCUS in clinical decision making and highlights the advantages of its utility in the ED.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".