Application of event-related potential P300 in the evaluation of cognitive dysfunction in patients with traumatic brain injury
Bibliographic record
Abstract
ObjectiveTo explore the application value of event-related potential P300 in the evaluation of cognitive dysfunction in patients with traumatic brain injury.MethodsFrom January to September 2021， a total of 36 patients with traumatic brain injury who were conservatively treated in the Neurosurgery Department of the Third Hospital of Mianyang and met the diagnostic criteria were selected as the experimental group. And 36 participants were recruited from the family members and carers of other patients in the hospital as the control group. Oddball paradigm was used to measure the event-related potential P300. Montreal Cognitive Assessment （MoCA） and Mini-Mental State Examination （MMSE） were used to assess the cognitive function of the subjects. The latency and amplitude of P300， MoCA and MMSE scores were compared between two groups. The detection rates of P300 latency， MoCA and MMSE on cognitive dysfunction in patients with traumatic brain injury were compared.ResultsMoCA and MMSE scores in experimental group were lower than those in control group ［（18.08±4.29） vs. （27.36±1.20）， （22.53±3.54） vs. （28.11±1.09）， t=-12.510， -9.041， P<0.05］. The latency of P300 in experimental group was higher than that in control group ［（406.08±26.95）ms vs. （367.08±22.50）ms， t=6.665， P<0.05］， and the amplitude was lower than that in control group ［（7.76±0.90）μV vs.（9.87±0.99）μV， t=-9.459， P<0.05］. In experimental group， the positive detective rate of P300 latency and MoCA on cognitive dysfunction were higher than that in MMSE （χ2=5.675， 7.604， P<0.05）.ConclusionEvent-related potential P300 can be used as an objective clinical indicator for evaluating cognitive dysfunction in patients with traumatic brain injury.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".