Domain-specific versus generalized cognitive screening in acute stroke
Bibliographic record
Abstract
Cognitive assessments after stroke are typically short form tests developed for dementia that generates pass/fail classifications (e.g. the MoCA). The Oxford Cognitive Screen (OCS) provides a domain-specific cognitive profile designed for stroke survivors. This study compared the use of the MoCA and the OCS in acute stroke with respect to symptom specificity and aspects of clinical utility. A cross-sectional study with a consecutive sample of 200 stroke patients within 3 weeks of stroke completing MoCA and OCS. Demographic data, lesion side and Barthel scores were recorded. Inclusivity was assessed in terms of completion rates and reasons for non-completion were evaluated. The incidence of cognitive impairments on both the MoCA and OCS sub-domains was calculated and differences in stroke specificity, cognitive profiles and independence of the measures were addressed. The incidence of acute cognitive impairment was high: 76% of patients were impaired on MoCA, and 86% demonstrated at least one impairment on the cognitive domains assessed in the OCS. OCS was more sensitive than MoCA overall (87 vs 78% sensitivity) and OCS alone provided domain-specific information on prevalent post-stroke cognitive impairments (neglect, apraxia and reading/writing ability). Unlike the MOCA, the OCS was not dominated by left hemisphere impairments but gave differentiated profiles across the contrasting domains. The OCS detects important cognitive deficits after stroke not assessed in the MoCA, it is inclusive for patients with aphasia and neglect and it is less confounded by co-occurring difficulties in these domains.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".