Performance of the Large Language Models on the Chinese National Nurse Licensure Examination: Cross-Sectional Evaluation Study
Bibliographic record
Abstract
Background: Large language models (LLMs) are increasingly explored in nursing education, but their capabilities in specialized, high-stakes, culturally specific examinations, such as the Chinese National Nurse Licensure Examination (CNNLE), remain underevaluated, making rigorous evaluation crucial before their adoption in nursing training and practice. Objective: This study aimed to evaluate the performance, accuracy, repeatability, confidence, and robustness of 4 LLMs on the CNNLE. Methods: Four LLMs (Sider Fusion [Vidline Inc], GPT-4o [OpenAI], Gemini 2.0 Pro [Google DeepMind], and DeepSeek V3) were tested on 237 multiple-choice questions from the 2024 CNNLE. Accuracy and repeatability were assessed using 2 prompting strategies. Confidence was evaluated via self-ratings (1-10 scale) and robustness via repeated adversarial prompting. Results: DeepSeek V3 and Gemini 2.0 Pro demonstrated significantly higher overall accuracy (ranging from 199/237 to 209/237; >83%) compared to GPT-4o and Sider Fusion (ranging from 151/237 to 166/237; <71%). However, all LLMs showed suboptimal repeatability (highest at 206/237; <87% consistency). Critically, poor confidence calibration was evident; most models showed high confidence often mismatching actual accuracy (Sider Fusion: P=.01; GPT-4o: P=.03; and Gemini 2.0 Pro: P=.049). A stability-flexibility trade-off paradox was also observed. Conclusions: While some LLMs show promising accuracy on the CNNLE, fundamental reliability limitations (poor confidence calibration and inconsistent repeatability) hinder safe application in nursing education and practice. Future LLM development must prioritize trustworthiness and calibrated reliability over surface accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".