Predictors of quality of life among individuals who have a lower limb amputation
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to identify factors that predict an individual's subjective quality of life (QoL) after having a lower limb amputation. DESIGN: Cross-sectional descriptive study design. SUBJECTS: A total of 415 unilateral, above knee (27.0%) and below knee (73.0%) amputees with an average age of 61.9 years (SD = 15.7) who had lost their limb related to vascular (53.0%) or non-vascular (47.0%) etiology. METHODS: Medical chart review, questionnaires (Frenchay Activities Index, Interpersonal Support Evaluation List, the Center for Epidemiology Studies - Depression scale, Prosthetic Evaluation Questionnaire mobility subscale, and the Activities-specific Balance Confidence Scale) and a QoL Visual Analogue Scale were assessed using multiple linear regression analysis. RESULTS: The analysis revealed seven significant factors (depression, perceived prosthetic mobility, social support, comorbidity, prosthesis problems, age and social activity participation) as predictors of subjects' perceived QoL. Depression explained 30% of the variation, while the full model explained 42% of the variation. CONCLUSION: Several modifiable characteristics influence QoL after lower limb amputation including depression and participation in daily living. This finding suggests the importance of addressing individuals' affective status to regain or maintain QoL.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".