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 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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".