Comfort and function remain key factors in upper limb prosthetic abandonment: findings of a scoping review
Bibliographic record
Abstract
PURPOSE: Rates of prosthetic device abandonment are dramatically high; however, the reasons behind abandonment are less understood. A scoping review was conducted to explore the current state of the literature on why individuals abandon upper limb prosthetic devices and consider how these reasons have evolved historically. MATERIALS AND METHODS: A systematic search of the literature identified 123 articles. After reviewing the articles using predetermined inclusion and exclusion criteria, nine relevant articles were included in the final review. The included articles covered passive, body-powered and myoelectric prosthetic devices. RESULTS: Across time, reasons for abandonment could be broadly categorized into comfort and function. Weight, temperature and perspiration were among the most common and persistent comfort-related reasons for abandonment. Regarding function, studies-reported abandonment was attributed to key concerns about control and sensory feedback, whereby participants may feel more functional without their device. CONCLUSIONS: In agreement with the previous literature, lack of comfort and function remain persistent reasons for upper limb prosthesis abandonment. Up-to-date research on reasons for abandonment of upper limb prosthetic devices is lacking, and recent prosthesis advancements have not been included in studies of device use, adoption and abandonment. Therefore, future work should explore reasons for abandonment in contemporary upper limb prosthetic devices. By understanding the reasons for prosthetic device abandonment, clinicians, therapists and researchers can use this information to proactively mitigate future upper limb prosthetic device abandonment. Findings from this review can be used to guide future prosthetic device development to improve these areas of concern and satisfy user needs.IMPLICATIONS FOR REHABILITATIONBy understanding the reasons for prosthetic device abandonment, clinicians, therapists and researchers can use this information to proactively mitigate future upper limb prosthetic device abandonment.The findings from this review can be used to guide future prosthetic device development to improve areas of concern and satisfy user needs.
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 | 0.018 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".