Diagnosing Pulmonary Embolism During Pregnancy
Bibliographic record
Abstract
TOPIC IMPORTANCE: Pulmonary embolism (PE) is one of the leading causes of pregnancy-related deaths in high-income countries. Maternal mortality from PE has been attributed to delayed recognition and investigation. The diagnosis of PE may be challenging, as its early signs and symptoms may overlap with physiological changes of pregnancy. As such, promptly ruling out suspected PE using diagnostic testing is of paramount importance. This narrative review provides a contemporary overview of risk assessment tools, diagnostic modalities, counseling needs, and existing best practice guidance for the diagnosis of PE in pregnancy. REVIEW FINDINGS: The revised Geneva score and the pregnancy-adapted YEARS algorithm are promising risk stratification methods that have been found to be safe and effective to support the diagnosis of PE in pregnancy. CT pulmonary angiography and ventilation perfusion scans have comparable safety and effectiveness profiles. Iodinated contrast agents administered for CT pulmonary angiography in pregnant patients with suspected PE are not associated with risks of neonatal adverse events. Pregnant patients may experience distress about fetal health during diagnostic testing, underscoring the importance of counseling to help in decision-making and improve the quality of care. Recent guidelines have supported the use of clinical prediction rules. Both imaging modalities are considered safe in pregnancy, with some guidance advising to choose between the 2 tests based on chest radiography results. SUMMARY: The choice of diagnostic testing should be based on equipment availability, the ability to perform testing in a timely manner, clinical urgency, chest radiography results, and suspicion of alternative diagnoses.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".