AI that teaches: an evidence-based GPT model to improve medical student understanding of pulmonary function tests
Bibliographic record
Abstract
Implication Statement This study explores the integration of an augmented Generative Pre-trained Transformer (GPT) tool with curated scientific sources to enhance the learning of pulmonary function test (PFT) interpretation in pre-clerkship medical education. Our findings suggest that this approach offers notable improvements in accuracy, reliability, and the quality of explanations compared to existing tools, such as Out-of-Box GPT and USMLE Q-Banks. The PFT learning assistant can support medical students in navigating common learning barriers, provide a personalized and scalable approach to evidence-based medical education Énoncé des implications de la recherche Cette étude explore l'intégration d'un outil de transformation générative pré-entraînée (GPT) enrichi de ressources scientifiques sélectionnées afin d'améliorer l'apprentissage de l'interprétation des épreuves fonctionnelles respiratoires (EFR) dans la formation médicale préclinique. Nos résultats suggèrent que cette approche offre des améliorations notables en termes de précision, de fiabilité et de qualité des explications par rapport aux outils existants, tels que le GPT standard et les banques de questions USMLE. Cet assistant d'apprentissage des EFR peut aider les étudiants en médecine à surmonter les obstacles d'apprentissage courants et propose une approche personnalisée et adaptable de la formation médicale fondée sur les preuves.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".