Domain-specific versus generalized cognitive screening in acute stroke
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it