Where Are We on Proprioception Assessment Tests Among Poststroke Individuals? A Systematic Review of Psychometric Properties
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Proprioception is often impaired in poststroke individuals. This is a significant issue since altered proprioception is associated with poorer physical function outcomes poststroke. However, there is limited consensus on the best tools for assessing proprioception and support for their widespread use by clinicians. The objective is to appraise the psychometric properties of each test used to assess proprioception in poststroke individuals. METHODS: A systematic search was performed according to PRISMA guidelines using the databases MEDLINE, Cochrane Library, PEDro, DiTa, and BioMedicalCentral for articles published up to January 2021. RESULTS: Sixteen studies of low methodological quality were included. Sixteen different proprioception assessment tests were extracted. The proprioception portion of the Fugl-Meyer Assessment Scale was found to be the most valid and reliable tool for screening patients in clinical settings. Although no real gold standard exists, the technological devices demonstrated better responsiveness and measurement accuracy than clinical tests. Technological devices might be more appropriate for assessing proprioception recovery or better suited for research purposes. DISCUSSION AND CONCLUSIONS: This review revealed low-quality articles and a paucity of tests with good psychometric properties available to clinicians to properly screen and assess all subcomponents of proprioception. In perspective, technological devices, such as robotic orthoses or muscle vibration, may provide the best potential for assessing the different subcomponents of proprioception. Further studies should be conducted to develop and investigate such approaches.Video, Supplemental Digital Content 1, available at:http://links.lww.com/JNPT/A388.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".