Danish Reference Material for the Oxford Cognitive Screen:A Stroke Specific Screening Tool
Bibliographic record
Abstract
Introduction Every year, approximately 15’000 people have a stroke in Denmark. Cognitive deficits following stroke are common and have negative consequences on quality of life, return to work and likelihood of developing depressive symptoms. Cognitive deficits must be identified in order to provide appropriate interventions and care. There is no international golden standard for screening cognitive deficits following stroke so dementia screening tools are often used. The Oxford Cognitive Screen (OCS) has been developed specifically to identify clinically important cognitive deficits following stroke. It consists of 10 sub-tests that are structured around five cognitive domains: Attention and Executive function, Language, Memory, Number processing, and Praxis, and only takes 15 minutes to administer. The aim of the study was to translate OCS into Danish and standardize the OCS on a sample of healthy Danish participants. Methods The OCS was translated from English to Danish and culturally adapted for a Danish context. After piloting on five patients, data from approximately 100 healthy participants stratified for age and education was collected. Participants were screened for dementia with the Montreal Cognitive Assessment. Results We present the Danish version of the OCS as well as reference material based on the data collected. Influence of age and education on OCS performance are discussed. Conclusions The Oxford Cognitive Screen is a screening tool that is specifically designed for stroke patients. It is quick to administer and is designed to maximise patient inclusion. The Danish translation and reference material provided will enable its use in a Danish context.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.030 |
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".