Assessing the clinical probability of pulmonary embolism during pregnancy: The Pregnancy‐Adapted Geneva (PAG) score
Bibliographic record
Abstract
BACKGROUND: The diagnosis of pulmonary embolism (PE) in pregnant women represents an ongoing challenge. As in the general population, the first step in pregnant women with suspected PE consists of assessing clinical pre-test probability (PTP). However, no dedicated clinical decision rule has been developed in this population. OBJECTIVE: To propose a new version of the Geneva score adapted to pregnant women with suspected PE. METHODS: Data from a multicenter, prospective management outcome study including 395 women with suspected PE, in whom PTP was assessed using the Geneva score, were used. We first removed items which were present in none of the patients (cancer, age >65 years). Receiver operating characteristic (ROC) curve analysis was then performed for quantitative variables and the optimal threshold defined. The obtained Pregnancy-Adapted Geneva Score (PAG Score) comprised seven items, including an age 40 years or older and a heart rate >110 beats per minute. RESULTS: The PAG Score showed a high discriminative power to identify patients with a low, intermediate, or high PTP, associated with increasing prevalence of PE, 2.3%, 11.6%, and 61.5%, respectively. The ROC curves showed an area under the curve of 0.795 for the PAG Score compared to 0.684 for the Geneva score. CONCLUSION: In pregnant women with suspected PE, the PAG Score shows a high discriminative power to identify patients at low, intermediate, or high PTP. It has the strength of being a fully objective decision rule, is clinically relevant, easy to compute, and should now be tested in a prospective outcome study.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".