PedsCases: A Learning Module for an Approach to Juvenile Idiopathic Arthritis for Medical Students
Bibliographic record
Abstract
Abstract Introduction PedsCases is a comprehensive web-based educational tool that focuses on the core objectives of undergraduate pediatric education with extensive student involvement. PedsCases was created for and by medical students and provides an opportunity for active self-directed learning in pediatrics. The learning modalities available include questions, flash-card type quizzes, multistep clinical cases, and podcasts. Methods This learning module includes a podcast, written script, reference chart, and self-assessment tool that allow medical students to develop an approach to juvenile idiopathic arthritis (JIA). The podcast and script provide an overview of the seven different subtypes of JIA. The module also includes some real case examples to help solidify key concepts about the different subtypes of JIA. There is also an accompanying self-assessment that students can use to test their new knowledge and understanding of JIA in general, as well as the different subtypes of JIA. Also included is a quick reference guide that students can use throughout the learning module or on the hospital wards. Results The deployment of PedsCases has been empirically evaluated. Both cases and podcasts help medical students to achieve their desired learning objectives and improve student-assessed competency while cases improve student knowledge. Since 2008, PedsCases has had over 55,000 visitors from 150 countries with over 375,000 podcast downloads. PedsCases has published 14 previous learning modules in MedEdPORTAL. Discussion The content found on PedsCases.com is aligned with the Canadian National Undergraduate Curriculum in Pediatrics, which has been developed by the Pediatric Undergraduate Program Directors of Canada; thus, it is a key educational tool for pediatric educators and clerkship directors to use to complement the national curriculum and help learners to achieve the outlined competencies.
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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 | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.172 | 0.066 |
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".