Assessing the capability of large language models in answering pediatric critical care board-style questions
Daniela Chanci, Ronald Moore, Henry P Foote, Matthew A Goldstein, Karan R Kumar, Alexandre T Rotta, Christoph P Hornik, Marybeth Burriss-West, Makenzie Hamilton, Noah Stanco, Monica Alvarez, Andrew Brown, Margot Johnson, Rishikesan Kamaleswaran · 2026
Read Assessing the capability of large language models in answering pediatric critical care board-style questions by Daniela Chanci, Ronald Moore, Henry P Foote, Matthew A Goldstein, Karan R Kumar, Alexandre T Rotta, Christoph P Hornik, Marybeth Burriss-West, Makenzie Hamilton, Noah Stanco, Monica Alvarez, Andrew Brown, Margot Johnson, Rishikesan Kamaleswaran (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13522507.txt.
Description
Daniela Chanci and collaborators test open-source language models on pediatric critical care questions in a 2026 benchmark study. One hundred board-style multiple-choice questions across six domains were written by specialists. Eight models ranging from small to large were scored for accuracy and for quality of clinical reasoning, and six trainee physicians answered the same set for comparison.
The largest model scored highest at seventy-eight percent, yet a much smaller model reached seventy-five percent with nearly equal reasoning scores. Both performed close to the trainee group overall. Models did well on ethics questions and poorly on calculations. The authors argue that smaller, locally run models merit further study for secure decision support and trainee education, provided calculation weaknesses are addressed.
Catalog record
| CREATOR | Daniela Chanci, Ronald Moore, Henry P Foote, Matthew A Goldstein, Karan R Kumar, Alexandre T Rotta, Christoph P Hornik, Marybeth Burriss-West, Makenzie Hamilton, Noah Stanco, Monica Alvarez, Andrew Brown, Margot Johnson, Rishikesan Kamaleswaran |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44636 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |