Residual limb support devices on wheelchairs for people with transtibial amputations: A scoping review and survey of rehabilitation professionals in Nova Scotia
Bibliographic record
Abstract
PURPOSE: To determine what research evidence exists for the use of residual limb supports (RLSs) for people with transtibial amputations and to describe clinicians' use of such supports in Nova Scotia. METHODS: Scoping review of published and gray literature using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews as a guide and an anonymous online and paper-based clinician survey. RESULTS: We identified 22 publications meeting criteria for review. Seventeen (77%) of the publications were practice guidelines or systematic reviews about care of people with lower-limb amputations, 4 (18%) involved research about the design of stump supports, and 1 (5%) researched the use of supports. Generally, the use of RLSs was recommended (e.g., to prevent contractures, control edema, and to provide comfort), but many authors acknowledged that the evidence was weak, and additional evidence in support of these treatment goals could not be found. We received 44 survey responses from health care professionals involved with the care of people with transtibial amputations in Nova Scotia. Of the 43 health care professionals who responded to the question "… what percent of patients/clients with transtibial amputations do you estimate receive stump supports …," the mean (standard deviation) was 86.1% (21.1). The most common reasons for recommending a stump support were to prevent knee contracture (38 [86.4%]), and to prevent swelling (13 [29.5%]). CONCLUSIONS: Most clinicians who provide services to people with amputations in Nova Scotia believe that RLSs have benefits such as the prevention of contractures, the reduction of edema, and improved patient comfort. However, there is little high-quality research evidence to support their use. There is a need to perform the necessary research or to modify practice guidelines.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".