Can Pregnancy-Adapted Algorithms Avoid Diagnostic Imaging for Pulmonary Embolism?
Bibliographic record
Abstract
The low prevalence of pulmonary embolism (PE) among pregnant patients presenting with suspected PE implies that most of these patients will be found not have the disease. Given this low prevalence, excluding PE in this population has necessitated the use of sensitive and specific diagnostic imaging, such as computed tomography pulmonary angiography or ventilation-perfusion scanning. Recent studies suggest that a clinical prediction rule with D-dimer testing can also be used to exclude a subset of pregnant patients with suspected PE without the need for diagnostic imaging. The YEARS criteria, which consist of clinical signs and symptoms of deep venous thrombosis, hemoptysis, and PE as the most likely diagnosis (a subjective variable), combined with selective D-dimer levels, seem to safely exclude up to one-third of these patients without imaging. The revised Geneva rule using objective variables, combined with nonpregnancy cutoffs for D-dimer levels, offers some promise, although fewer patients avoided imaging (14%). These recent studies provide evidence in support of radiation avoidance for some patients; however, for most, imaging remains the only option. Future studies should focus on improving the safety and techniques of imaging modalities, in addition to improving the specificity of D-dimer testing and objective prediction rules. Studies assessing patients' and physicians' values, preferences, and risk perceptions are also required to assist clinicians in shared decision making when counseling pregnant patients with suspected PE.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".