PedsCases - A Learning Module of Acute Stridor for Medical Students
Bibliographic record
Abstract
Abstract This learning module includes a podcast and three cases that review the common causes of acute stridor in children, as well as the pathophysiology and treatment of those causes. It is intended for medical students of all years. The podcast covers foreign body inhalation, anaphylaxis, epiglottitis, croup, and bacterial tracheitis. A script of the podcast has also been included. In addition to the podcast and script, the module includes cases discussing croup, epiglottitis, and bacterial tracheitis. Each case begins with the initial presentation of a child with stridor. The student is required to answer multiple-choice questions that focus on establishing a differential diagnosis and confirming the diagnosis. Next, the student lists a management plan for the specific cause of the stridor. The correct answer for each question is explained once the question has been answered. By working first through the podcast and then through the cases, the student will have the ability to recognize some of the acute causes of stridor and develop an initial management plan. This module is a part of PedsCases, a series of comprehensive web-based educational tools that focus 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. PedCases has been used in 96 different countries, and there have been over 10,000 downloads of the available 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.117 | 0.058 |
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".