Sex and gender differences in upper extremity prosthesis rejection: A review of literature
Bibliographic record
Abstract
Prosthesis rejection is a significant barrier to rehabilitation of persons with upper limb difference. Many individual factors can affect device rejection, including a person's sex or gender. The objective of this narrative review was to explore the reported differences between the sexes and genders in upper limb prosthesis rejection. This review considered peer-reviewed, published research studies in which the study population were adults (aged 18 and older) who had unilateral or bilateral limb difference (any level) of any etiology with current, past, or no history of prosthetic device usage. Using identified keywords, index terms, and a peer-reviewed search filter, the literature was searched in MEDLINE, Embase, and PsycInfo. The reasons for rejection, disuse, or abandonment of prosthetic devices were extracted, with the focus on reported differences between sex and genders. After searching, 29 articles were selected for full-text review and 15 were included. Only 5 of 15 articles examined differences between the sexes. Women tend to reject upper extremity prostheses more than men both before and after being fit with a device; device characteristics, such as weight and cosmesis, do not appear to be appropriately designed for women; and there may not be adequate consideration of the goals for women with limb difference(s). There is inadequate reporting of sex and gender in the literature on prosthesis rejection; future studies should report and explore these factors to determine whether the needs of the full population with limb loss are being met.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".