D.3 Peripheral nerve injuries related to walking aid use: a systematic review
Bibliographic record
Abstract
Background: Walking aids such as crutches, canes and walkers are used by 2 million Canadians. Repetitive weight-bearing with walking aids may cause upper limb peripheral nerve injury. The objectives of this review were to: 1) identify types of nerve injuries reported with walking aids; 2) report electrodiagnostic findings; 3) identify typical treatment strategies; and 4) determine expected recovery time for such injuries. Methods: MEDLINE, EMBASE, CINAHL and Cochrane Library were searched for primary data in English published between 1950-2022. Abstracts were reviewed independently by 2 authors. Full-text reviews were independently conducted by 2 authors. Results: The search identified 3746 abstracts, 43 of which underwent full-text review. 31 studies were included. There were 144 cases of peripheral nerve injury. Crutches caused the most injuries (n=21 studies). The ulnar nerve was most commonly injured (n=27 cases). Improper walking aid fit was identified as a risk factor in 74% of cases. Stopping walking aid use was the most common treatment strategy (n=10 studies). Follow-up reports (n=20) indicated 65% of patients experienced recovery at 6 months. Conclusions: Improper walking aid fit and use were identified as major injury risk factors. A national program to teach patients and clinicians how to use walking aids may reduce injury risk.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".