Spinal Cord Injury
Conditions
Brief summary
This observational and methodological study aims to compare the performance of large language models in generating electrode contact configuration recommendations for epidural electrical stimulation in spinal cord injury. Five standardized synthetic spinal cord injury scenarios will be presented to four large language models: ChatGPT-4o, Claude, Grok 3, and Gemini 2.5 Pro. Each model will receive the same standardized prompt. The generated responses will be anonymized and evaluated independently by experts with experience in spinal cord injury rehabilitation and epidural electrical stimulation. The responses will be assessed in five main areas: clinical accuracy, technical feasibility, safety awareness, consistency with current clinical guidance, and completeness of the response. Agreement between expert evaluators will also be examined. No real patients, human participants, clinical interventions, or personal health data are included in this study. The study is designed to explore the potential and current limitations of large language models as artificial intelligence-based clinical decision-support tools in neurorehabilitation.
Interventions
OTHERChatGPT-4o Large Language Model
The large language model receives five standardized synthetic spinal cord injury scenarios using an identical standardized prompt and generates recommendations for epidural electrical stimulation electrode contact configuration mapping. No intervention is administered to human participants.
OTHERClaude Large Language Model
The large language model receives five standardized synthetic spinal cord injury scenarios using an identical standardized prompt and generates recommendations for epidural electrical stimulation electrode contact configuration mapping. No intervention is administered to human participants.
OTHERGrok 3 Large Language Model
The large language model receives five standardized synthetic spinal cord injury scenarios using an identical standardized prompt and generates recommendations for epidural electrical stimulation electrode contact configuration mapping. No intervention is administered to human participants.
OTHERGemini 2.5 Pro Large Language Model
The large language model receives five standardized synthetic spinal cord injury scenarios using an identical standardized prompt and generates recommendations for epidural electrical stimulation electrode contact configuration mapping. No intervention is administered to human participants.
Sponsors
Istanbul Gelisim University
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* Responses generated for one of the five predefined standardized synthetic spinal cord injury scenarios. * Responses generated using the identical standardized prompt specified in the study protocol. * Responses generated by one of the four prespecified large language models. * Complete responses available for expert evaluation.
Exclusion criteria
* Responses generated using prompts that differ from the standardized study prompt. * Incomplete, interrupted, or technically corrupted model outputs. * Duplicate responses or outputs not corresponding to a predefined synthetic scenario. * Any response generated using real patient-identifiable or personal health information.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Clinical Accuracy Score of Large Language Model Responses | At the time of expert evaluation, within 1 week after study initiation | Clinical accuracy of the epidural electrical stimulation electrode contact configuration recommendations generated by each large language model will be independently evaluated by expert reviewers using a 5-point Likert-type rating scale. Higher scores indicate greater clinical accuracy of the generated recommendations. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Technical Feasibility Score of Large Language Model Responses | At expert evaluation, within 1 week after study initiation | The technical feasibility of epidural electrical stimulation electrode contact configuration recommendations generated by each large language model will be independently evaluated by expert reviewers using a 5-point Likert-type rating scale. Higher scores indicate greater technical feasibility and applicability of the generated recommendations. |
| Safety Awareness Score of Large Language Model Responses | At expert evaluation, within 1 week after study initiation | The safety awareness demonstrated in the epidural electrical stimulation electrode contact configuration recommendations generated by each large language model will be independently evaluated by expert reviewers using a 5-point Likert-type rating scale. Higher scores indicate greater recognition and consideration of relevant safety issues. |
| Clinical Guideline Consistency Score of Large Language Model Responses | At expert evaluation, within 1 week after study initiation | The consistency of the generated epidural electrical stimulation electrode contact configuration recommendations with current clinical guidance will be independently evaluated by expert reviewers using a 5-point Likert-type rating scale. Higher scores indicate greater consistency with current clinical guidance and relevant evidence-based recommendations. |
| Response Completeness Score of Large Language Model Responses | At expert evaluation, within 1 week after study initiation | The completeness of the epidural electrical stimulation electrode contact configuration recommendations generated by each large language model will be independently evaluated by expert reviewers using a 5-point Likert-type rating scale. Higher scores indicate more complete and comprehensive responses. |
Countries
Turkey (Türkiye)
Outcome results
None listed