The mini-Oxford cognitive screen (Mini-OCS): A very brief cognitive screen for use in chronic stroke
Bibliographic record
Abstract
Introduction: No stroke-specific cognitive screen currently exists for community-dwelling chronic stroke survivors, with primary care and community settings relying on dementia tools which often do not consider specific post-stroke impairments. The Oxford Cognitive Screen (OCS) was developed for use in acute stroke, but its administration time is prohibitive for brief screening. Here, we aimed to develop, standardise and psychometrically validate the Mini-Oxford Cognitive Screen (Mini-OCS), a brief (<8 min) cognitive screen aimed for use in chronic stroke. Method: Existing full OCS data for 464 English participants who were ⩾6 months post-stroke were analysed for the possibility of a short-form. Theoretical choices were made to adapt the short-form to be suitable for use in chronic stroke. The Mini-OCS was then completed by 164 neurologically healthy controls ( M age = 68.66; SD = 12.18, M years of education 15.40; SD = 3.64, 61% female), and 89 chronic stroke survivors ( M age = 69.86; SD = 14.83, M years education = 14.29; SD = 4.01, 44.94% female, M days since stroke = 597.02; SD = 881.12, 78.57% ischaemic, Median NIHSS = 6.5 (IQR = 4–11)). In addition, the original OCS, the Montreal Cognitive Assessment, and an extended neuropsychological battery were administered. Psychometric properties of the Mini-OCS were evaluated via construct validity and retest reliability. Findings: Normative data for the Mini-OCS is provided and known-group discrimination demonstrates increased sensitivity in the memory and executive function domains compared to the OCS. The Mini-OCS further met all appropriate benchmarks for evidence of retest reliability and construct validity. Discussion and conclusion: The Mini-OCS is a short-form standardised cognitive screening tool with initial evidence of good psychometric properties for use in a chronic stroke population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".