Clinical usefulness of cognitive evoked potentials to assess cognitive deficits in cerebrovascular diseases
Bibliographic record
Abstract
Abstract Background: The aim of the present study is to evaluate the clinical applicability and usefulness of cognitive evoked potentials (CEP) to identify a cognitive deficit in patients with cerebrovascular diseases (CVD). Methods: The P3 latencies, amplitudes and latency to amplitude ratios (LAR) of CEP were measured in 25 healthy control subjects and 35 inpatients with CVD. The association of CEP with variables including age, sex, mini‐mental state examination (MMSE) score, CVD types, loci of hemiplegic limbs, duration, education, brief psychiatric rating scale (BPRS), instrumental activities of daily living (IADL) and daily living function assessment (DLFA) was also analyzed. Results: (i) The P3 latencies (447.87 ± 113.06 msec) and LAR (65.83 ± 43.25) were prolonged in CVD (P < 0.05), while the amplitudes (8.18 ± 2.51 µV ) were not changed; (ii) the P3 latencies (537.31 ± 101.14msec) and LAR (94.89 ± 46.44 in CVD with a MMSE score <24 were prolonged, and the amplitudes (6.45 ± 1.98 µV ) were reduced (P < 0.05, respectively); (iii) the BPRS, IADL and DLFA in CVD with a MMSE score <24 were different from MMSE ≥24 (P < 0.05); (iv) there was no difference in CEP between CVD caused by infaction and hemorrhage; (v) the P3 latencies were correlated positively with age, BPRS and IADL, while negatively with MMSE and DLFA. The amplitudes were correlated positively with MMSE and DLFA, while negatively with age, BPRS and IADL. The LAR were correlated positively with age, BPRS and IADL, while negatively with MMSE and DLFA; and (vi) on analyzing association of CEP with variables in CVD with MMSE <24, the P3 latencies were correlated positively with BPRS and DLFA, while negatively with MMSE and DLFA. The amplitudes were positively correlated with age. The LAR were positively correlated with IADL. Conclusions: The P3 latencies and LAR of CEP seemed to be useful clinical measures to assess cognitive disorders in CVD as well as in vascular dementia.
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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 | 0.000 | 0.002 |
| 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".