Diagnostic Management of Pregnant Women With Suspected Pulmonary Embolism
Bibliographic record
Abstract
Pulmonary embolism (PE) is one of the most common causes of severe morbidity and mortality during pregnancy. PE diagnosis during pregnancy remains a true challenge for all physicians, as many of the symptoms and signs associated with PE are often reported during physiological pregnancy. The fear of missing a PE during pregnancy leads a low threshold of suspicion, hence to a low prevalence of confirmed PE among pregnant women with suspected PE. This means that most pregnant women with suspected PE do not have the disease. Until recently, international guidelines suggested thoracic imaging in all pregnant women with suspected PE. Two recent prospective management outcome studies based on clinical probability assessment, D-dimer measurement, venous compression ultrasonography of the lower limbs (CUS) and computed tomography pulmonary angiography (CTPA) proved the safety of such strategies, with a very low failure rate. For the first time, these studies also demonstrated that the association of a clinical prediction rule and D-dimer measurement allowed a safe exclusion of PE in a significant proportion of pregnant women, without the need for radiating imaging tests. These two prospective studies pave the way to further improvements in the diagnostic strategies. Indeed, both specific clinical prediction rules and possibly D-dimer cutoffs adapted to pregnant women could help to further reduce the proportion of patients needing thoracic imaging. As an imaging test will still ultimately be necessary in a significant proportion of women, further technical advances in CT scans protocols could reduce the radiation dose to both the fetus and the mother, an important step to reassure clinicians. Finally, educational efforts should be encouraged in the future to pass the challenge of implementing these validated diagnostic strategies in everyday clinical practice.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".