CETARS/CAR Practice Guideline on Imaging the Pregnant Trauma Patient
Bibliographic record
Abstract
Imaging of pregnant patients who sustained trauma often causes fear and confusion among patients, their families, and health care professionals regarding the potential for detrimental effects from radiation exposure to the fetus. Unnecessary delays or potentially harmful avoidance of the justified imaging studies may result from this understandable anxiety. This guideline was developed by the Canadian Emergency, Trauma and Acute Care Radiology Society (CETARS) and the Canadian Association of Radiologists (CAR) Working Group on Imaging the Pregnant Trauma Patient, informed by a literature review as well as multidisciplinary expert panel opinions and discussions. The working group included academic subspecialty radiologists, a trauma team leader, an emergency physician, and an obstetriciangynaecologist/maternal fetal medicine specialist, who were brought together to provide updated, evidence-based recommendations for the imaging of pregnant trauma patients, including patient safety aspects (eg, radiation and contrast concerns) and counselling, initial imaging in maternal trauma, specific considerations for the use of fluoroscopy, angiography, and magnetic resonance imaging. The guideline strives to achieve clarity and prevent added anxiety in an already stressful situation of injury to a pregnant patient, who should not be imaged differently.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".