Severe anaphylactic reaction to contrast agent: teams are well prepared but should simulate the situations regularly
Bibliographic record
Abstract
OBJECTIVES: Anaphylactic reactions are dramatic and life-threatening. According to international guidelines, the immediate intramuscular administration of adrenaline is the most important first step for acute management. The aim of this study is to determine whether doctors can recognize and treat severe anaphylactic reactions to contrast agents adequately. METHODS: In this study, 95 doctors were interviewed between January and June 2023 in European clinics that are not affiliated with the authors. Ninety-five doctors from radiology, internal medicine, and anaesthesia departments were randomly selected for interviews. A video was prepared simulating a male patient developing a severe anaphylactic reaction during CT after intravenous administration of an iodinated contrast medium. After the video, 95 doctors were interviewed (59 radiologists, 19 internists, and 17 anaesthesiologists). The doctors were asked 3 questions: (1) What is the diagnosis? (2) What is the therapy of choice? (3) Can you demonstrate the correct way to operate the adrenaline autoinjector? RESULTS: All 95 doctors made the correct diagnosis (100%). Sixty-three of 95 physicians (66%) were uncertain regarding the appropriate first-line therapy. This was observed across all three medical specialties (internal medicine, anaesthesiology, and radiology) (P = .64). Sixty-five physicians (68%) had difficulties triggering the autoinjection system successfully. CONCLUSIONS: Acute anaphylaxis is life-threatening, but there is uncertainty among professional groups about initiating acute management. Refresher training should be considered to ensure timely and appropriate treatment of the condition when it occurs. ADVANCES IN KNOWLEDGE: This study highlighted significant gaps in physicians' real-world readiness to manage acute anaphylaxis, despite all doctors correctly diagnosing the condition.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".