Assessment of unilateral spatial neglect post stroke in acute care hospitals : are we neglecting neglect?
Bibliographic record
Abstract
Timely and accurate assessment of unilateral spatial neglect (USN) post stroke is a critical component of best practice, given that a recent Clinical Practice Guidelines for stroke has recommended the standardized assessment of USN within 48 hours of regaining consciousness following a stroke. This multi-centered, retrospective study using data from medical charts of a representative sample of individuals admitted to 10 Ontario acute care hospitals from July 15th to December 15th 2002, examined the prevalence, timing and frequency of use of standardized assessments to evaluate USN post stroke. Out of the 248 subjects who should have received a USN assessment, 37.5% received an assessment; only 13.31% with a standardized visual perception tool and of these, only 0.81% (n = 2) with a standardized tool specific to USN assessment. All clients receiving a standardized assessment were evaluated for USN in the near extrapersonal space, the hemispace within reaching distance of the patient: no patient received a standardized assessment for USN in the personal space or far extrapersonal space. Three standardized visual perception tools that include a USN component were used: the Clock Drawing Test (n = 22), the Ontario Society of Occupational Therapists (OSOT) Perceptual Evaluation (n = 8) and the Motor-Free Visual Perception Test (n = 1). Only 8 (3.23%) of the 248 clients were screened with a standardized tool within the 2-day critical period as recommended by Stroke Guidelines. Reassessment was rare, even in those with detected USN, such that only 1 subject was ever reassessed with a standardized tool.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".