The Clinical Utility of a Diagnostic Imaging Algorithm Incorporating Low-Dose Perfusion Scans in the Evaluation of Pregnant Patients With Clinically Suspected Pulmonary Embolism
Bibliographic record
Abstract
PURPOSE OF THE REPORT: The aim of this study was to determine the proportion of pregnant patients with a clinical suspicion of pulmonary embolism and a normal chest radiograph who require further evaluation with perfusion scintigraphy alone compared with both perfusion scintigraphy and computed tomography (CT). PATIENTS AND METHODS: All patients who had a low-dose perfusion lung scan as part of a clinical imaging algorithm to assess for clinically suspected pulmonary embolism in pregnant patients at 3 regional hospitals from September 2009 to February 2011 were retrospectively reviewed. The proportion of patients requiring a low-dose perfusion-only lung scan was compared with the proportion requiring further evaluation with both a low-dose perfusion scan and a CT scan to complete the algorithm. RESULTS: Seventy-four (74) patients were included. Sixty-one (61/74; 82.4%) patients had a normal low-dose perfusion-only scan and did not require further imaging. Thirteen (13/74; 17.6%) patients demonstrated an abnormal perfusion scan and required further imaging with a CT scan. One patient (1/74; 1.4%) was diagnosed with pulmonary embolism. CONCLUSIONS: Our results suggest that for pregnant patients with a normal chest radiograph, pulmonary embolism can be excluded in 82.4% of patients with a low-dose perfusion scan alone.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".