Lower limb prosthesis utilisation by elderly amputees
Bibliographic record
Abstract
The goal of prosthetic rehabilitation is to compensate for the loss of a limb by amputation by, in the case of a lower limb, encouraging walking, and to achieve the same level of autonomy as prior to the amputation. However, because of difficulties walking, elderly amputees may use their prosthesis to a greater or lesser degree or simply stop using it during the rehabilitation period. The objective of this research was to study factors such as physical and mental health, rehabilitation, physical independence and satisfaction with the prosthesis to understand why amputees use their prosthesis or not. The sample was composed of 65 unilateral vascular amputees 60 years old or over living at home. The information was collected from medical records, by telephone interview and by mail questionnaire. Prosthesis use was measured by a questionnaire on amputee activities developed by Day (1981). Eighty-one per cent (81%) of the subjects wore their prosthesis every day and 89% of this group wore it 6 hours or more per day. Less use of the prosthesis was significantly related to age, female gender, possession of a wheelchair, level of physical disability, cognitive impairment, poorer self-perceived health and the amputee's dissatisfaction. A multiple regression analysis showed that satisfaction, not possessing a wheelchair and cognitive integrity explained 46% of the variance in prosthesis use.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".