Validation of the UK English Oxford Cognitive Screen-Plus in Sub-Acute and Chronic Stroke Survivors.
Bibliographic record
Abstract
IntroductionStroke survivors are routinely screened for cognitive impairment with tools that often fail to detect subtle impairments. The Oxford Cognitive Screen-Plus (OCS-Plus) is a brief tablet-based screen designed to detect subtle post-stroke cognitive impairments. We examined its psychometric properties in two stroke cohorts (subacute: <3 months post-stroke, chronic: >6 months post-stroke).Patients and MethodsThis study included 347 stroke survivors (mean age = 73 years; mean education = 13 years; 43.06% female presenting; 74.42% ischaemic stroke). The OCS-Plus was completed by 181 sub-acute stroke survivors and 166 chronic stroke survivors. All participants also completed the Oxford Cognitive Screen (OCS) and a subset completed the Montreal Cognitive Assessment (MoCA) and further neuropsychological tests. ResultsFirst, convergent construct validity of all OCS-Plus tasks against task-matched standardized neuropsychological tests was confirmed. Second, we evaluated divergent construct validity of all OCS-Plus subtasks. Third, we report the sensitivity and specificity of each OCS-Plus subtask compared to neuropsychological test performance. Fourth, we found that OCS-Plus detected cognitive impairments in a large proportion of those classed as unimpaired on MoCA and OCS.Discussion and ConclusionThe OCS-Plus provides a valid screening tool for sensitive detection of subtle cognitive impairment in stroke patients. Indeed, the OCS-Plus detected subtle cognitive impairment at a similar level to validated neuropsychological assessments and exceeded detection of cognitive impairment compared to standard clinical screening tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".