Frequency of domain-specific cognitive impairment in sub-acute and chronic 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
BACKGROUND: Functional contributions of cognitive impairment may vary by domain and severity. OBJECTIVE: (1) To characterize frequency of cognitive impairment by domain after stroke by severity (mild: -1.5 ≤ z-score < -2; severe: Z ≤ -2) and time (sub-acute: < 90d; chronic: 90d-2yrs); and (2) To assess the association of cognitive impairment with function in chronic stroke. METHODS: Cognitive function was characterized among 215 people with sub-acute or chronic stroke (66.8 years, 43.3% female). Z-scores by cognitive domain were determined from normative data. Function was defined as the number of IADLs minimally independent. RESULTS: 76.3% of sub-acute and 67.3% of chronic stroke participants had cognitive impairment in ≥ 1 domain (p-for-difference = 0.09). Severe impairment was most common in psychomotor speed (sub-acute: 53.5%; chronic: 33.7%). Impairment in executive function was common (sub-acute: 39.5%; chronic: 30.7%) but was usually mild. Severe impairment in psychomotor speed, visuospatial function, and language and any impairment in executive function and memory was associated with IADL impairment (p < 0.03). CONCLUSIONS: Mild cognitive impairment is common after stroke but is not associated with functional disability. Impairment in psychomotor speed, executive function, and visuospatial function is common and associated with functional impairment so should be a focus of screening and rehabilitation post-stroke.
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