PedsCases - A Learning Module for the Evaluation of a Child With Failure to Thrive for Medical Students
Bibliographic record
Abstract
Abstract This resource is a podcast-based learning module that allows medical students to develop an approach to the evaluation of a child with failure to thrive. The case features a 2-month-old boy who presents with a failure to thrive. The case then helps the students develop an approach to history and physical examination of the child while ensuring that students understand the definition and potential causes of failure to thrive. Useful investigations are discussed; a shotgun approach is discouraged. This resource is a part of PedsCases, a comprehensive web-based educational tool that focuses on the core objectives of undergraduate pediatric education with extensive student involvement. It was created for and by medical students and provides an opportunity for active self-directed learning in pediatrics. The learning modalities include questions, flash card–type quizzes, multistep clinical cases, and podcasts. PedsCases has been integrated into the third-year undergraduate pediatric medical education curriculum at the University of Alberta. It is one of the main sources recommended to students to assist in covering the core objectives of the clinical pediatric rotation and in preparing for the final examinations. Since the focus of medical education has shifted towards independent learning, PedsCases has become a complementary educational tool and has filled a niche.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.013 |
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".