Patient Outcomes After Implementation of Pulmonary Embolism Response Teams in the United States of America: A Review of the Literature
Bibliographic record
Abstract
Abstract Introduction: Pulmonary embolism (PE) is the 3rd leading cause of cardiovascular death, with approximately 200,000 - 900,000 new cases annually in the United States of America (USA). PE is categorized into one of three categories (low, intermediate, and high risk) based on the severity of hemodynamic compromise, right ventricular (RV) enlargement, and laboratory values. Individuals with low-risk PE generally do well with anticoagulation alone. Those with intermediate-risk PE pose a challenge, and prior research has been inconsistent with treatment recommendations. Due to this challenge, the Pulmonary Embolism Response Team (PERT) was established in 2012 to provide a multidisciplinary approach for individualizing risk stratification and tailoring treatment plans. This literature review aims to analyze the effect of implementing PERT on patient outcomes, focusing on mortality rates and patient care efficiency metrics (time to diagnosis, time to anticoagulation and time to triage). Methods: A systematic literature review using PubMed and Google Scholar was conducted to analyze mortality rate and patient outcomes, specifically time to diagnosis, time to triage and timing of initiation of anticoagulation after the implementation of the PERT. Results: Six articles were analyzed and found to have inconsistent findings regarding mortality rate after the implementation of PERT. Of those that found statistically significant reductions in mortality rate, a correlation was noted with improved patient care efficiency metrics. Conclusion: PERT provides individualized risk stratification and management of treatment decisions, which is particularly important for intermediate PE. Emphasis on the timing of diagnosis and initiation of anticoagulation is crucial in reducing mortality rates. Continued research is essential to evaluate the long-term effects of PERT on mortality rates.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".