Bridging knowledge gaps in paediatric chronic urticaria through a video-based educational tool
Bibliographic record
Abstract
BACKGROUND: There is a lack of patient educational resources about chronic urticaria (CU). AIMS: To develop and test the effectiveness of an education tool to help paediatric patients and their families better understand CU and its management. METHODS: From July 2020 to May 2022, paediatric patients with a history of CU who presented to the allergy outpatient clinics at our institution were recruited. Consenting families and patients were asked to complete five questions related to the definition, causes and management of CU at the time of presentation to the clinic. Participants were shown a 5-min animated video addressing the main knowledge gaps about CU. At the end of the video, participants were redirected to the same five questions to respond again. The scores were recorded as a proportion of correct answers (range 0·0-1·0). RESULTS: In total, 53 patients [30 girls (56·6%), 23 boys (43·4%); mean age 9·7 ± 5·1 years, range 1·4-18·5 years] were recruited. The mean baseline pre-video education questionnaire score was 0·67 ± 0·2 (range 0·2-1·0), while the mean post-video score was 0·94 ± 0·1 (range 0·4-1·0), a mean score difference of 0·27, which was statistically significant (P < 0·001). At the 1-year follow-up, 14 (26·4%) patients answered the questionnaire again to assess retention of knowledge; the mean score was 0·83 ± 0·2 (range 0·2-1·0). CONCLUSIONS: Our educational video was successful in educating patients and their families to better understand urticaria. Future studies should aim to optimize patient education through nontraditional tools such as videos, and compare knowledge gain using different methods of education.
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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 | 0.021 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".