Diagnosis of Pulmonary Embolism during Pregnancy
Bibliographic record
Abstract
Although rare, pulmonary embolism (PE) remains one of the most common causes of severe maternal morbidity and mortality during pregnancy. Among pregnant women with suspected PE, the prevalence of confirmed disease is far lower than in the general population, reflecting the fear of missing the diagnosis and a low threshold to suspect PE in this setting. Two prospective management outcome trials have recently assessed two different diagnostic algorithms based on the assessment of clinical probability, D-dimer, venous compression ultrasonography of the lower limbs (CUS), and computed tomography pulmonary angiography (CTPA). Both demonstrated the safety of such strategies to exclude PE, with a very low failure rate defined as the rate of subsequent 3-month venous thromboembolism in women left untreated after a negative work-up. These studies were also the first to prospectively demonstrate the safety of negative D-dimer associated with a clinical prediction rule to exclude PE without any chest imaging. Pregnant women are known to be a subgroup at particularly high risk of inappropriate diagnostic management, so the implementation of such validated diagnostic strategies in clinical practice should represent a high priority goal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".