A SYSTEMATIC REVIEW OF THE ASSESSMENT AND ASSOCIATION OF UNILATERAL NEGLECT AND PROPRIOCEPTION AFTER STROKE
Bibliographic record
Abstract
Background and Aims: To determine if people with unilateral neglect (UN+) after stroke have more frequent or severe proprioceptive deficits than those without unilateral neglect (UN-) after stroke.Methods: The MEDLINE, Embase, Scopus, CINAHL and Web of Science databases were searched from inception to December 2018 using an a priori search strategy. Two independent reviewers screened abstracts and full texts. Two reviewers then independently extracted data from each full text. A third reviewer resolved disagreements at each step. Risk of bias was assessed using the AXIS Quality Assessment tool for cross-sectional studies, and the Newcastle-Ottawa Scale for cohort studies. For full protocol see PROSPERO, registration number CRD42018086070. Results: One-hundred and sixty-seven (n=167) abstracts were identified, of which fifty-four (n=54) were eligible for full text screening. A total of eighteen (n=18) papers were included in the review. We found low to moderate quality (AXIS median 14, IQR 12-15) evidence that UN+ have more frequent and severe proprioceptive deficits than UN-. There were fifteen (n=15) different reported UN assessments, and thirteen (n=13) different measures of proprioception. Only two (n=2) studies used an assessment capturing UN present in all possible domains.Conclusions: The evidence is limited by the large heterogeneity of assessments and level of study quality. However, more severe proprioceptive impairment is implicated in UN. We found that neither UN nor proprioception are consistently and rigorously assessed and thus, this is likely also true in clinical practice. Future high-quality investigation of proprioception in UN is warranted to form the foundation for targeted treatment strategies.
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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 | 0.020 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".