New evidence in diagnosis of pulmonary embolism during pregnancy
Bibliographic record
Abstract
Diagnosis of pulmonary embolism (PE) in pregnancy is notoriously difficult and lacking high quality evidence. Three studies (DiPEP, ARTEMIS and CT-PE-Pregnancy) evaluating a systematic approach to PE diagnosis have recently been published. DiPEP is a retrospective case-control study that found a poor utility of clinical decision rules or D-dimer testing for PE diagnosis in pregnancy. ARTEMIS and CT-PE-Pregnancy are well conducted prospective management studies that proposed two algorithms with different clinical decision rules and D-dimer criteria for the diagnosis of PE in pregnancy. They included few events in high risk patients, which makes difficult the assessment of both algorithm's safety in women with a high probability of PE. Considering this new evidence, D-dimer testing might be useful to avoid radiation imaging in pregnant women considered at low risk for PE. In contrast, a negative D-dimer cannot be considered sufficiently safe to rule out PE when clinicians estimate that PE is the most likely diagnosis.
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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 | 0.030 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".