D-Dimer Use and Pulmonary Embolism Diagnosis in Emergency Units: Why Is There Such a Difference in Pulmonary Embolism Prevalence between the United States of America and Countries Outside USA?
Bibliographic record
Abstract
OBJECTIVE: Although diagnostic guidelines are similar, there is a huge difference in pulmonary embolism (PE) prevalence between the United States of America (US) and countries outside the USA (OUS) in the emergency care setting. In this study, we prospectively analyze patients' characteristics and differences in clinical care that may influence PE prevalence in different countries. METHODS: An international multicenter prospective diagnostic study was conducted in a standard-of-care setting. Consecutive outpatients presenting to the emergency unit and suspected for PE were managed using the Wells score, STA-Liatest® D-Dimers and imaging. RESULTS: The prevalence of PE in the study was 7.9% in low and moderate risk patients. Among the 1060 patients with low or moderate pre-test probability (PTP), PE prevalence was four times higher in OUS (10.7%) than in the US (2.5%) (P < 0.0001). The mean number of imaging procedures performed for one new PE diagnosis was 3.3 in OUS vs 17 in the US (P < 0.001). Stopping investigation in the case of negative D-dimers (DD combined) with low/moderate PTP was more frequent in OUS (92.7%) than in the US (75.7%) (P < 0.01). Moreover, the use of imaging was much higher in the US (44.4% vs 19.2% in OUS) in the case of moderate PTP combined with negative DD. CONCLUSION: Differences between US and OUS PE prevalence in emergency setting might be explained by differences in patients' characteristics and mostly in care patterns. US physicians performed computed tomographic pulmonary angiography more often than in Europe in cases of low/moderate PTP combined with negative DD. TRIAL REGISTRATION: ClinicalTrials.gov NCT01221805.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".