Comparison of nonmicroprocessor knee mechanism versus C-Leg on Prosthesis Evaluation Questionnaire, stumbles, falls, walking tests, stair descent, and knee preference
Bibliographic record
Abstract
This study compared subjects' performance with a nonmicroprocessor knee mechanism (NMKM) versus a C-Leg on nine clinically repeatable evaluative measures. We recorded data on subjects' performance while they used an accommodated NMKM and, following a 90-day accommodation period, the C-Leg in a convenience sample of 19 transfemoral (TF) amputees (mean age 51 +/- 19) from an outpatient prosthetic clinic. We found that use of the C-Leg improved function in all outcomes: (1) Prosthesis Evaluation Questionnaire scores increased 20% (p = 0.007), (2) stumbles decreased 59% (p = 0.006), (3) falls decreased 64% (p = 0.03), (4) 75 m self-selected walking speed on even terrain improved 15% (p = 0.03), (5) 75 m fastest possible walking speed (FPWS) on even terrain improved 12% (p = 0.005), (6) 38 m FPWS on uneven terrain improved 21% (p < 0.001), (7) 6 m FPWS on even terrain improved 17% (p = 0.001), (8) Montreal Rehabilitation Performance Profile Performance Composite Scores for stair descent increased for 12 subjects, and (9) the C-Leg was preferred over the NMKM by 14 subjects. Four limited community ambulators (Medicare Functional Classification Level [MFCL] K2) increased their ambulatory functional level to unlimited community ambulation (MFCL K3). Objective evaluative clinical measures are vital for justifying the medical necessity of knee mechanisms for TF amputees. Use of the C-Leg improves performance and quality of life and can increase MFCL and community ambulation level.
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 | 0.001 | 0.002 |
| 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".