The German version of the Oxford Cognitive Screen ( <scp>D‐OCS</scp> ): Normative data and validation in acute stroke and a mixed neurological sample
Bibliographic record
Abstract
Given the frequency of stroke worldwide, tools for neuropsychological assessment of patients with acute stroke are needed to identify cognitive impairments, guide rehabilitation efforts and allow for a prognosis of outcome. However, requirements for assessment tools for acute cognitive deficits differ substantially from tests for chronic neuropsychological impairments and screening tools for suspected dementia. The Oxford Cognitive Screen (OCS) has been developed as a quick to administer neurocognitive screening for acute neurological patients providing information on various cognitive domains. It is available in different languages. The present study reports cut-off scores, parallel-test reliability and concurrent validity of the German version (D-OCS). Following standardized language adaptation and translation, the D-OCS was administered to 100 healthy individuals to generate cut-off scores (5th percentile). Subsequently, 88 neurological patients were assessed with both versions of the D-OCS as well as other tests to evaluate reliability and validity of the D-OCS subscales. In a further study, the D-OCS was compared to the MoCA test in 65 acute stroke patients revealing comparable sensitivity but also differences between both tools. The cut-off scores were comparable to other international versions of the OCS. Intraclass correlations were highly significant and document reliability of the D-OCS subtests. Scores on subtests correlated significantly with independent tests securing validity. Comparison with the MoCA revealed comparable sensitivity and specificity. The D-OCS is a reliable and valid assessment tool well suited for patients with acute stroke. Differences to the MoCA test are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".