Comparing Consequences of Right and Left Unilateral Neglect in a Stroke Rehabilitation Population
Bibliographic record
Abstract
OBJECTIVE: This article details right and left unilateral neglect (UN) in a stroke rehabilitation population. DESIGN: This prospective observational cohort study documented hemipersonal neglect and hemispatial neglect in 309 of 325 stroke rehabilitation patients consecutively admitted over a 28-month period. Shoulder-hand complications, safety concerns, length of stay, discharge function, and discharge destination were documented. RESULTS: Of the 85 with right UN and 113 with left UN, 17.7% had expressive aphasia, and 17.7% had mixed or receptive aphasia. Hemispatial neglect was associated with hemianopsia (29.2% and 31.8% for right and left, respectively). Having both hemipersonal neglect and hemispatial neglect was related to greater safety risk (46.9% vs. 24.3%), greater incidence of shoulder-hand complications (28.3% vs. 9.9%), lower FIM scores (>10 points lower), longer length of stay (8 days), and less likelihood of discharge to home (67.3% vs. 87.4%) than subjects without UN. Results were similar for those with right and left UN. CONCLUSIONS: Right and left UN occur after stroke, can be detected even in the presence of aphasia, and are associated with shoulder-hand problems, lower discharge function, and lower likelihood of discharge home. Having both hemispatial neglect and hemipersonal neglect impacts people more than having either type of UN alone.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".