The Impact of Diabetic Neuropathy on Activities of Daily Living, Postural Balance and Risk of Falls - A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to discuss a compilation of the currently available literature regarding the impact of diabetic neuropathy (DN) on activities of daily living (ADL), postural stability, and risk of falls. METHODS: A systematic electronic search strategy was conducted on PubMed/MEDLINE database, Cochrane Library, and Embase in March 2020. This narrative review included clinical cross-sectional studies assessing ADL, postural balance, and falls in adults with DN. All studies underwent a quality assessment based on the Newcastle Ottawa scale developed to assess cross-sectional studies. RESULTS: Forty-two studies were identified. A total of 37 studies evaluated postural stability in DN, 10 studies assessed fall accidents, and three studies assessed ADL in individuals with DN. Seven studies assessed both postural stability and fall accidents, and one study assessed postural stability and ADL. Each of the studied outcome variables was assessed separately. Based on a quality assessment, eight studies were excluded resulting in an evaluation of 34 studies. CONCLUSIONS: Diabetic neuropathy has a negative impact on postural balance and gait kinematics combined with an increased fall risk. Because of the few number of studies available, we were unable to evaluate the impact of DN on ADL. Our findings are in concordance with previous reviews, supporting the evidence for DN as a critical measure negatively impacting postural stability and fall risk in individuals with diabetes. Further clinical investigative studies are needed.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".