Can the large language model <scp>ChatGPT</scp> ‐4omni predict outcomes in adult patients with status epilepticus?
Bibliographic record
Abstract
OBJECTIVE: Large language models (LLMs) have recently gained attention for clinical decision-making and diagnosis. This study evaluates the performance of the recently updated LLM Chat Generative Pre-Trained Transformer-4omni (ChatGPT-4o) in predicting clinical outcomes in patients with status epilepticus and compares its prognostic performance to the Status Epilepticus Severity Score (STESS). METHODS: This retrospective single-center cohort study was performed at the University Hospital Basel (tertiary academic medical center) from January 2005 to December 2022. It included consecutive adult patients (≥18 years of age) with a diagnosis of status epilepticus. The primary outcome was survival at hospital discharge, and the secondary outcome was return to premorbid neurological function at hospital discharge. The performance characteristics of ChatGPT4-o (sensitivity, specificity, Youden Index) were evaluated and compared to those of the STESS. RESULTS: Of 760 patients, 689 patients (90.7%) survived to discharge, and 317 survivors (41.7%) regained their premorbid neurological function at discharge. ChatGPT-4o predicted survival in 567 of 760 patients (74.6%), of which 45 died. ChatGPT-4o predicted death in 193 of 760 patients (25.4%), of which 167 survived, resulting in a sensitivity of 75.8% and a specificity of 36.6% (Youden Index 0.12, 95% confidence interval [CI] 0-.28) for predicting survival. ChatGPT-4o predicted return to premorbid neurologic function in 249 of 760 patients (32.8%), of which 112 did not return to their premorbid neurological function. ChatGPT-4o predicted no return to premorbid function in 511 of 760 patients (67.2%), of which 180 returned to their premorbid function, resulting in a sensitivity of 43.2% and a specificity of 74.7% (Youden Index .12, 95% CI .08-.28) for predicting return to premorbid neurological function. There was no difference in the prognostic performance of ChatGPT-4o and the STESS. A second round of prompting did not increase the predictive performance of ChatGPT-4o. SIGNIFICANCE: ChatGPT-4o unreliably predicts outcomes in patients with status epilepticus. Clinicians should refrain from using ChatGPT-4o for prognostication in these patients.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".