Title:Revision of the Diagnostic Methods Used for the Detection of Pulmonary Embolism in Pregnant Women at the “Centre Hospitalier Universitaire De Sherbrooke”
Bibliographic record
Abstract
Introduction: We aim to describe the incidence of pulmonary embolism in pregnant patients in which this diagnosis was suspected in our hospital and to compare it with the 5% incidence found in medical literature. We also want to describe the use of serum D-dimer assay in our hospital and assess whether there has been an increase in its use over time for these patients, particularly since the appearance of the ARTEMIS study in March 2019. Methods: We collected data from our local computerized clinical record for all pregnant patients aged 18 to 45 who underwent an investigation for pulmonary embolism between January 2015 and November 2019. Results: Of the 186 of these patients, 4 (2.2%; 95% CI [0.84% - 5.40%]) were diagnosed with a confirmed pulmonary embolism. There was no statistically significant difference with the value of 5% found in the literature (p value at 0.07). Before March 2019, serum D-dimer was measured in 57.7% of pregnant patients in whom there was suspicion of pulmonary embolism. After March 2019, D-dimer was measured in 82.6% of these patients, representing a statistically significant increase in their use before and after March 2019, with a p-value of 0.02. Conclusions: These results demonstrate that there is no statistically significant difference between the incidence of pulmonary embolism in pregnant women in which this diagnosis is suspected in our hospital and that found in medical literature. In recent years, some studies have shown that the serum D-dimer assay could be useful for the purpose of identifying pregnant patients who would benefit from imaging that could expose them to ionizing radiation, and indeed we have found an increase in the use of D-dimer in the diagnostic approach for pulmonary embolism in pregnant patients in our hospital after March 2019.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".