Combined prediction of mismatch negativity and P300 for the cognitive function in traumatic brain injury patients
Bibliographic record
Abstract
Objective To investigate the predictive value of mismatch negativity (MMN) combined with P300 for cognitive dysfunction at 6 months after traumatic brain injury (TBI) in adult patients. Methods A total of 75 adult TBI patients admitted to Xiangya Hospital, Central South University from January 2021 to January 2024 were enrolled. MMN and P300 were monitored within 7d after TBI. Cognitive function was assessed at 6 months after TBI using the Telephone Interview for Cognitive Status (TICS), with a score of<27 indicating cognitive dysfunction. Univariate and multivariate Logistic regression analyses were used to identify influencing factors for cognitive dysfunction at 6 months after TBI. Receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) was calculated to evaluate the predictive performance of the identified factors. Results Lower absolute value of Fz MMN amplitude (OR=0.426, 95%CI: 0.188-0.968; P=0.041) and Cz P300 amplitude (OR=0.399, 95%CI: 0.188-0.847; P=0.017) were identified as risk factors for cognitive dysfunction at 6 months after TBI. ROC curve showed that the AUC for the absolute value of Fz MMN amplitude was 0.713 (95%CI: 0.595-0.830, P=0.002), with an optimal cutoff value of 2.37 μV. The AUC for the absolute value of Cz P300 amplitude was 0.752 (95%CI: 0.641-0.863, P=0.000), with an optimal cutoff value of 3.28μV. When these 2 indicators were combined for ROC curve, the combined indicator yielded an AUC of 0.781 (95%CI: 0.676-0.886, P=0.000). Delong test revealed no statistically significant differences in AUC between the combined indicator and the absolute value of Fz MMN amplitude (Z=1.574, P=0.115) or the absolute value of Cz P300 amplitude (Z=0.939, P=0.348), suggesting comparable predictive performance among the 3 indicators. Conclusions MMN combined with P300 may serve as a favorable indicator for predicting cognitive dysfunction at 6 months after TBI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".