Bridging Knowledge Gaps in Anaphylaxis Management Through a Video-Based Educational Tool
Bibliographic record
Abstract
Introduction: We aimed to develop and test the effectiveness of an education tool to help pediatric patients and their families better understand anaphylaxis and its management, and to improve current knowledge and treatment guidelines adherence. Methods: From June 2019 to May 2022, 128 pediatric patients with history of food-triggered anaphylaxis who presented to the allergy outpatient clinics at the study institution were recruited. Consenting families were asked to complete 6 questions related to the triggers, recognition, and management of anaphylaxis at the time of presentation to the clinic. Participants were shown a 5-min animated video on the causes, presentation, and management of anaphylaxis. At the end of the video, the participants were redirected to the same 6 questions to respond again. The scores were recorded in proportion of correct answers (minimum 0.0; maximum 1.0). Results: The mean age of the patients was 5.8 ± 4.5 years (range: 0.5–18.8 years). The majority were males (70 patients; 54.7%). The mean baseline prevideo education questionnaire score was 0.76 ± 0.2 (range: 0.3–1.0), whereas the mean follow-up score was 0.82 ± 0.2 (range: 0.3–1.0). This score difference of 0.06 was statistically significant ( P < 0.001). There were no significant associations between change in scores and age or gender of the participants. Conclusion: Our video teaching method was successful in educating patients and their families to better understand anaphylaxis and its management at the moment of the clinical encounter. Retention of knowledge at long-term follow-up should be assessed.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".