PedsCases - A Learning Module for Kawasaki Disease for Medical Students
Bibliographic record
Abstract
Abstract This resource is a learning module designed to provide medical students with an approach to the diagnosis and treatment of Kawasaki disease in the pediatric population. The module includes a case that centers around an 11-month-old child who presents with a history of fever and rash. Using this initial information, the case involves multiple-choice questions to review the differential diagnosis of Kawasaki disease as well as the clinical findings needed to diagnose this disease. In addition, the case discusses the potential complications of the disease as well as the treatment of Kawasaki disease and the necessary follow-up once the diagnosis is made. The learning module also includes a podcast that supplements the material presented in the case. The podcast explains the clinical importance and pathology of Kawasaki disease, as well as the diagnostic criteria, current treatment regime, and follow-up necessary for Kawasaki disease. A script for the podcast is also included. This resource is a part of PedsCases, a comprehensive web-based educational series that focuses on the core objectives of undergraduate pediatric education with extensive student involvement. PedsCases was created for and by medical students to provide an opportunity for active self-directed learning in pediatrics. The learning modalities available include questions, flash card–type quizzes, multistep clinical cases, and podcasts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.179 | 0.069 |
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".