PedsCases - A Learning Module for Kawasaki Disease for Medical Students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it