Assessment of unilateral spatial neglect post stroke in Canadian acute care hospitals: are we neglecting neglect?
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence, timing and frequency of use of standardized and non-standardized assessments to detect unilateral spatial neglect in acute care patients post stroke. DESIGN AND SETTING: Multicentred, retrospective study on medical charts from 10 randomly selected acute care hospitals in Ontario, Canada. SUBJECTS: Three hundred and twenty-four randomly selected medical charts of adult subjects with a primary diagnosis of stroke admitted in 2002 to the participating acute care hospitals. RESULTS: Out of 248 subjects who should have been assessed, 38% received some form of unilateral spatial neglect assessment. Only 13% were assessed with a standardized assessment and of these, 4% within 48 h post stroke or within 48 h of the patient regaining consciousness as recommended by clinical practice guidelines for stroke. Bivariate analysis found significant associations between severity of cognitive impairment and being ever assessed, as well as between the severity of motor deficits of the upper extremity and being ever assessed. CONCLUSION: Routine standardized assessment of unilateral spatial neglect during the acute care phase post stroke was not incorporated into daily practice in this study sample.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".