Exploring sex differences in the needs and priorities of people with lower limb amputations: An adaptation of the Prosthesis Evaluation Questionnaire
Bibliographic record
Abstract
BACKGROUND: Females with lower limb amputations have different risk factors and lower success with their prostheses overall. Studying priorities of this population, specifically in how they differ between sexes, through survey methods may improve understanding of female-specific needs and inform sex-specific prosthetic design. OBJECTIVES: To adapt the Prosthesis Evaluation Questionnaire and use this to assess sex differences in needs and priorities of people with lower limb amputations (pLLAs). STUDY DESIGN: Cross-sectional questionnaire study. METHODS: A committee was formed to modify the Prosthesis Evaluation Questionnaire. The modified questionnaire was completed online by 26 pLLAs (13 females, 13 males). Sex differences in subscale and individual closed-question responses were analyzed using Mann-Whitney U tests. Sex differences in open-ended question responses were analyzed using affinity diagramming. RESULTS: Significant sex differences were found in subscale scores and separate closed questions, with resulting qualitative themes further suggesting sex-specific priorities and perspectives. Females reported lower satisfaction with prosthetic appearance, poorer overall ambulation abilities, and greater perception of social burden than males. Sex differences were also found in themes related to prosthesis satisfaction and other psychosocial factors including social adjustment. CONCLUSIONS: Findings demonstrated all-encompassing sex differences in the priorities and needs of pLLAs. This work can be used to better understand and target female's unique priorities through sex-specific considerations in research and prosthetic design.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".