The Korean Version of the Oxford Cognitive Screen (K-OCS) Normative Study
Bibliographic record
Abstract
OBJECTIVE: To generate a Korean version of the Oxford Cognitive Screen (K-OCS) and obtain cutoff scores that determine the impairment of each subdomain. Post-stroke cognitive impairment (PSCI) negatively impacts the rehabilitation process and independence in daily life. Its obscure manifestations require effective screening for appropriate rehabilitation. However, in most rehabilitation clinics, psychological evaluation tools for Alzheimer's dementia have been used without such considerations. The OCS is a screening assessment tool for PSCI and vascular dementia that can evaluate the cognitive domains most often affected by stroke, including language, attention, memory, praxis, and numerical cognition. It comprises 10 subtasks and enables quick and effective cognitive evaluation. METHODS: The K-OCS, which considers Korea's unique cultural and linguistic characteristics, was developed with the approval and cooperation of the original author. Enrollment of participants without disabilities was announced at Duksung Women's University, Yongin Sevrance Hospital, CHA Bundang Medical Center. The study was conducted between September 2020 and March 2022 on 97 male and female participants aged ≥30 years. RESULTS: All the 97 participants completed the task. In this study, the 5th percentile score was presumed to be the cutoff value for each score, and the values are provided here. The cutoff score for each OCS subtask was similar to that of the original British version. CONCLUSION: We suggest the usability of the K-OCS as a screening tool for PSCI by providing the cutoff value of each subtask.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".