Validation of Korean Version of the Oxford Cognitive Screen (K-OCS), a Post Stroke-Specific Cognitive Screening Tool
Bibliographic record
Abstract
OBJECTIVE: To establish and evaluate the validity of the recently developed Korean version of the Oxford Cognitive Screen (K-OCS), this study verified its reliability, validity, and diagnostic accuracy. METHODS: Between November 2021 and December 2023, we recruited 72 patients with stroke from our hospital who agreed to participate in the study. The patients were repeatedly tested using K-OCS by the same or different assessors to estimate inter- and intra-rater reliability. To demonstrate the validity and usability of K-OCS, the test results of screening tools currently used in clinical practice, including the Korean-Mini Mental State Examination and the Korean version of the Montreal Cognitive Assessment, were used in comparison analyses. RESULTS: The subtests of K-OCS demonstrated excellent inter-rater reliability (intra-class correlation coefficient [ICC]=0.914-0.998) and test-retest reliability (ICC=0.913-0.994). We found moderate-to-strong correlations for convergent validity for the subsets (r=0.378- 0.979, p<0.01), and low-to-moderate discriminant validity correlations. The optimal cut-offs estimated for the subtests of the K-OCS showed a good-to-high range of specificity (94.8%- 100%). The positive predictive value was 58.2%-100% and negative predictive value was 65.6%-98.4%. Sensitivity was estimated at 25.6%-86.9%. CONCLUSION: The results of this study indicate that K-OCS is a reliable and valid tool for screening cognitive impairment in patients post-stroke.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".