Injuries Associated with Crutch Use: A Narrative Review
Bibliographic record
Abstract
Crutches are commonly prescribed in physiatric settings to help offload the lower extremities. Crutch use, however, results in abnormal and repetitive weight-bearing through the upper extremities that may result in secondary injury. This narrative review investigates injury types and risk factors associated with crutch use in order to guide healthcare providers on injury prevention strategies. Medline, EMBASE, CINAHL, and the Cochrane Library were systematically reviewed for publications between 1950 and 2018 on neurological, musculoskeletal, or vascular complications associated with crutch use. Titles and abstracts (n = 2395) were screened by two authors. Sixty studies were eligible. Articles were reviewed for level of evidence, crutch type, participant characteristics, and injury characteristics. There were 42 axillary crutch studies, 12 forearm crutch studies, and 6 studies that did not specify crutch type. These studies incorporated 622 individuals, and most were case series or case reports (n = 54). Axillary crutch use was most commonly associated with axillobrachial arterial complications due to pressure from the axillary bar (n = 34). Forearm crutch use was most commonly associated with compressive neuropathies due to pressure from the forearm cuff (n = 6). Improper crutch fitting and/or use were identified as contributing factors to injury in 22 cases. Duration of crutch use and medical comorbidities also influenced the types of injuries seen. There are rare but potentially serious complications associated with crutch use. When prescribing crutches, clinicians should ensure they are properly fit, engage in appropriate gait training, be aware of common crutch-related injuries, and consider potential patient-specific injury risk factors in order to minimize injury risk.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".