Application of the P300 potential in cognitive impairment assessments after transient ischemic attack or minor stroke
Bibliographic record
Abstract
Background The aims of this study were to determine the relationships between changes inlatency and amplitude of the P300 event-related potential component and cognitive impairmentsin patients after a transient ischemic attack (TIA) or a minor stroke and to assess thesuitability of the P300 for screening for cognitive impairments.Material and Method Sixty-five TIA or minor stroke patients diagnosed at the NeurologyDepartment of Beijing Tiantan Hospital, Capital Medical University from June 2015 toDecember 2016 and 30 healthy people evaluated in the same period were included. Allpatients were examing neuropsychological scales and event-related potentials within7 ± 3 days of onset of the disease. The TIA/minor stroke group was divided into normal cognition group(NC) and cognitive impairment group. The cognitive impairment group was further divided into vascular cognitive impairment with no dementia(VCIND) group and vascular dementia (VD) group to analyze the relationship between P300 latency.Results The P300 latency at each recording electrode was longer in the NC and VCIND groups than healthy control group (P < 0.001), the P300 latency of VCIND group longer than NC group (P < 0.001). When the P300-Fz latency cut-off value was 358.6 ms,the sensitivity for diagnosing cognitive impairment in patients after TIA/minor stroke was 0.875 and the specificity was 0.765.Conclusions The P300 latency delay can be used to detect cognitive impairments in patients after TIA/minor stroke and the P300-Fz latency is more sensitive for diagnosing cognitiveimpairments in TIA/minor stroke 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 | 0.001 | 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 | 0.000 | 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".