Impact of prostheses on quality of life and functional status of transfemoral amputees in Tanzania
Bibliographic record
Abstract
Background: The rise of diabetes and traumatic injury has increased limb loss-related morbidity in low- and middle-income countries (LMICs). Despite this, the majority of amputees in LMICs have no access to prosthetic devices, and the magnitude of prosthesis impact on quality of life (QOL ) and function has not been quantified. Objectives: Quantify the impact of prostheses on QOL and function in Tanzanian transfemoral amputees. Method: A prospective cohort study was conducted. Transfemoral amputees at Muhimbili Orthopaedic Institute were assessed twice before and three times after prosthetic fitting using EuroQol-5D-3L (EQ-5D-3L), Prosthetic Limb Users Survey of Mobility (PLUS-M), 2-minute walk test (2MWT) and Physiologic Cost Index (PCI). Data were analysed for change over time. Subgroup analysis was performed for amputation aetiology (vascular or non-vascular) and prosthesis use. Results: Amongst 30 patients, EQ-5D, PLUS-M and 2MWT improved after prosthesis provision ( p < 0.001). EuroQol-5D increased from 0.48 to 0.85 at 1 year ( p < 0.001). EuroQol-5D and 2MWT were higher in non-vascular subgroup ( p < 0.030). At 1-year, 84% of non-vascular and 44% of vascular subgroups reported using their prosthesis ( p = 0.068). Conclusion: Prosthesis provision to transfemoral amputees in an LMIC improved QOL and function. This benefit was greater for non-vascular amputation aetiologies. Quality of life and function returned to pre-prosthesis levels with discontinued use of 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 | 0.001 |
| 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 | 0.000 | 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".