Exploring pregnant individuals’ counseling needs regarding urgent imaging to rule out pulmonary embolism
Bibliographic record
Abstract
Background: Computed tomography pulmonary angiogram and lung scintigraphy with ventilation/perfusion scan are needed to diagnose pulmonary embolism (PE) in pregnancy. Their associated ionizing radiation doses are considered safe in pregnancy. A standardized patient information tool may improve patient counseling and reduce testing hesitancy. Objectives: In this context, we sought to address 1) what patients want to know before undergoing these tests and 2) how they want the information to be provided to them. Methods: We used a qualitative descriptive methodology. We recruited pregnant participants at the McGill University Health Center in Montreal, Canada. Structured interviews explored information needs about PE and diagnostic imaging for PE. The interview transcripts' themes were analyzed with a hybrid deductive and inductive approach. Results: Of 21 individuals approached, 20 consented to participate. Four had been previously investigated for PE. Participants requested information about the risks associated with PE and radiation and their effects on maternal and fetal health. They preferred for radiation doses to be presented in comparison with known radiation thresholds for fetal harm. They suggested that a written tool should be developed using an accessible language. Participants also indicated that the tool would be integrated into their decision-making process, emphasizing a lower risk tolerance for their fetus than for themselves. Conclusion: This single-center group of pregnant patients wished to be informed about the risks of PE and radiation associated with imaging. A written tool could help put information into context and facilitate decision making. These new insights may be used to inform counseling.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".