A Child With Limb Pain: A Case-Based Learning Module and Teaching Resource for Pediatric Infectious Diseases
Bibliographic record
Abstract
INTRODUCTION: While case-based learning is an effective method, teaching resources in pediatric infectious diseases are limited. Thus, we developed a case-based learning module for a common pediatric infectious diseases topic, osteomyelitis. METHODS: This module contains two resource files, both meant to be printed. The case file contains questions with blank spaces for the trainee (medical student, junior resident) to complete. The case answers file is used as a guide by the teacher (attending physician, fellow, senior resident) and/or the trainee after working through the case. This resource may be used in one-to-one sessions, in a small-group setting, or as self-directed learning. The session is estimated to take 60-90 minutes. A suggested reading list is included. RESULTS: This resource was used in a small-group format with the pediatric residents of the Hospital for Sick Children in Toronto for an academic half-day session in November 2015. Twenty-eight learner evaluations were received. The session was rated a 4.8 out of 5 (with 5 = outstanding) and ultimately voted by the residents to be the best academic half-day session of the year. Compared to delivering a didactic lecture on the same topic, the facilitators found preparation time was reduced and interactions with the trainees were more engaging. All were willing to facilitate a similar session again. DISCUSSION: This resource was effective and popular from the perspective of both learners and teachers. Additional modules are currently under preparation in order to create a case-based teaching resource for pediatric infectious diseases.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.038 | 0.008 |
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".