Utilization of serum D-dimer assays prior to computed tomography pulmonary angiography scans in the diagnosis of pulmonary embolism among emergency department physicians: a retrospective observational study
Bibliographic record
Abstract
BACKGROUND: A variety of evidence-based algorithms and decision rules using D-Dimer testing have been proposed as instruments to allow physicians to safely rule out a pulmonary embolism (PE) in low-risk patients. OBJECTIVE: To describe the prevalence of D-Dimer utilization among emergency department (ED) physicians and its impact on positive yields and utilization rates of Computed Tomography Pulmonary Angiography (CTPA). METHODS: Data was collected on all CTPA studies ordered by ED physicians at three sites during a 2-year period. Using a chi-square test, we compared the diagnostic yield for those patients who had a D-Dimer prior to their CTPA and those who did not. Secondary analysis was done to examine the impact of D-Dimer testing prior to CTPA on individual physician diagnostic yield or utilization rate. RESULTS: A total of 2811 CTPAs were included in the analysis. Of these, 964 CTPAs (34.3%) were ordered without a D-Dimer, and 343 (18.7%) underwent a CTPA despite a negative D-Dimer. Those CTPAs preceded by a D-Dimer showed no significant difference in positive yields when compared to those ordered without a D-Dimer (9.9% versus 11.3%, p = 0.26). At the individual physician level, no statistically significant relationship was found between D-Dimer utilization and CTPA utilization rate or diagnostic yield. CONCLUSION: This study provides evidence of suboptimal adherence to guidelines in terms of D-Dimer screening prior to CTPA, and forgoing CTPAs in patients with negative D-Dimers. However, the lack of a positive impact of D-Dimer testing on either CTPA diagnostic yield or utilization rate is indicative of issues relating to the high false-positive rates associated with D-Dimer screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".