Factors Influencing Quality of Life Among Trauma-related Lower Limb Amputees
Bibliographic record
Abstract
Introduction: Lower limb amputation refers to the surgical removal of a portion or the entirety of a limb. This study assessed demographic and clinical characteristics of traumatic lower limb amputees and their QoL using the RAND SF-36 scale. Method: A cross-sectional study was conducted among 123 traumatic lower limb amputees using prostheses. Descriptive statistics, normality testing, reliability analysis, and correlation tests were performed for eight SF-36 subscales and composite scores. Results: Most participants were male (87%), aged 31–45 years (52%), and employed part-time (47.2%). Trans-tibial (56.1%) and trans-femoral (37.4%) amputations were most common, with right-sided involvement (61.8%) predominating. Mean Physical Component Summary (PCS) and Mental Component Summary (MCS) scores were 50.0 (SD = 7.39) and 50.0 (SD = 7.97), reflecting moderate QoL. Reliability analysis showed acceptable internal consistency (Cronbach’s α = 0.746). Education was positively associated with MCS (r = 0.314, p < 0.01), and level of amputation showed a weak positive correlation with MCS (r = 0.198, p < 0.05). No significant associations were observed between PCS/MCS and age, gender, employment status, or duration of prosthesis use. Conclusion: Traumatic amputees using prostheses show moderate QoL, with mental health outcomes influenced by education and level of amputation. The findings of the study emphasise the need for customised rehabilitation strategies with a greater focus on psychosocial support and vocational reintegration for traumatic prosthetic users. Key words: Trauma, Quality, Amputation, Limb, Influence, Prosthesis..
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 | 0.001 | 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) | 0.004 | 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".