REHABILITATION AFTER LOWER LIMB AMPUTATION: BETWEEN FUNCTIONAL RECOVERY AND QUALITY OF LIFE
Bibliographic record
Abstract
Below knee amputation is a big operation with far-reaching effects on the physical, psychological and social life of the patient.The most common reasons for an amputation are vascular disease, diabetes complications and traumatic injuries.In worldwide epidemiological research amputation rates scored highly with wide differences according to the country and access of medical service.For instance, one rate of 22.5 cases/100 000 per year was reported in Canada, with predomination in males over the age group of 65 years.The rate of amputation among American Indians is also three times the U.S. average.The use of procedures is extremely variable in European and Asian countries, with the highest rates being found for UK and the lowest for Japan.In the United States, hospitalization rates for non-traumatic amputation of a lower extremity decreased by over 50% from 1988 to 2008.In Germany and Italy also, prevalence of amputations among the elderly patients with complicated diabetes or PAD is very high.Objective: To review the scientific research regarding physical therapy of individuals with lower limb amputation.Materials and methods.A literature search was performed in the Scopus, PubMed and Google Scholar databases with search terms related to rehabilitation after LLA.Different dimensions of postamputation rehabilitation -physical, psychosocial, and emotional recovery -are useful for patients to start prior to surgery.Early physiotherapy, choice of prosthesis and psychological therapy are important in the rehabilitation process.End, virtual rehabilitation electrostimulation, mirror therapy and hydrotherapy have been demonstrated effective particularly in various stages of treatment.In the elderly, multidisciplinary treatment in conjunction with the individualization of loads and recovery phases with a slow progression can enhance success.It has been demonstrated
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".