Correlation Between Point Book-based Cognitive Tests and General Cognitive Tests of Brain Injury Patients
Bibliographic record
Abstract
Objective : This study aimed to analyze the correlation between the cognitive test results using the point book and the general cognitive test results, and to present assistance method for brain injury patients who are difficult to test due to problems such as language. Methods : This study included 30 brain injury patients who were admitted to a medical center. The Korean-Mini Mental State Examination, 2nd Edition (K-MMSE~2) and the Korean version of Montreal Cognitive Assessment (MoCA-K) were used as test tools. In each test, the correlation between the point book results and the general results was analyzed using the Spearman's rank correlation coefficient, and the comparison of the two results was analyzed using the Wilcoxon signed-rank test. Results : As a result of analyzing the correlation between the cognitive test results using the point book and the general cognitive test results, both K-MMSE~2 (ρ=.956) and MoCA-K (ρ=.949) showed statistically significant correlations (p<.01). As a result of comparing the two results in each test, all scores of K-MMSE~2 and MoCA-K showed no statistically significant difference (p>.05). Conclusion : This study showed a correlation between the cognitive test results using the point book and the general cognitive test results in brain injury patients. In the future, study through various subjects will be needed, and we hope that the point book will be used as an assistance method for the cognitive tests in clinical settings.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".