Comparing closed and open large language models on pediatric cardiology board exam performance
Bibliographic record
Abstract
Large language models (LLMs) have gained traction in medicine, but there is limited research comparing closed- and open-source models in subspecialty contexts. This study evaluated ChatGPT-4.0o and DeepSeek-R1 on a pediatric cardiology board-style examination to quantify their accuracy and discuss educational and clinical utility. ChatGPT-4.0o and DeepSeek-R1 were used to answer 88 text-based multiple choice questions across 11 pediatric cardiology subtopics from a Pediatric Cardiology Board Review textbook. DeepSeek-R1’s processing time per question was measured. ChatGPT-4.0o and DeepSeek-R1 achieved 70% (62/88) and 68% (60/88) accuracy, respectively ( p = 0.53). Subtopic accuracy was equal in 5 of 11 chapters, with each model outperforming its counterpart in 3 of 11. DeepSeek-R1’s processing time negatively correlated with accuracy ( r = -0.68, p = 0.02). ChatGPT-4.0o and DeepSeek-R1 were comparable in accuracy and approached the passing threshold on a pediatric cardiology board examination. While further development of LLMs is required for clinical integration into pediatric cardiology, these findings suggest the potential utility of these models as educational aids.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".