Application of Stroop Color-word Association Test and P300 Test in Recognition Function of the Patients with Asymptomatic Cerebral Infarction
Bibliographic record
Abstract
Objective To investigate the characteristics of Stroop Color-word Association Test(CWT)and event related potential P300 in patients with asymptomatic cerebral infarction(ACI) and to explore the assessment value of the CWT and P300 test for the recognition function of ACI patients. Methods 60 ACI out-patients and in-patients admitted to neurology department of People′s Hospital of Hainan Province were divided into control group(29 cases) and cognitive impairment(CI) group(31 cases) according to the scores of Montreal cognitive assessment(MoCA),with score≥26 as control group and score26 as CI group.The results of CWT and P300 test were analyzed and compared between the two groups. Results (1)CWT results:The application time and accurate reading in A and B part of CWT between CI group and control group showed no statistically significant differences(P0.05),but the application time and accurate reading in C part of CWT between the two groups showed statistically significant difference(P0.05).(2) P300 test results:The latency of P300 and amplitude between the two groups showed statistically significant difference((392.7±33.2)ms vs.(320.8±29.2) ms,(4.2±2.1)μV vs.(10.3±3.4)μV,t=6.9,7.2,P0.05). Conclusion Combined CWT and P300 test is conducive to early detection of cognitive impairment of the patients with ACI,so that early treatment can be performed to delay aggravation of symptoms and improve life quality.
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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".