Physiological Cost Index and Comfort Walking Speed in Two Level Lower Limb Amputees Having No Vascular Disease
Bibliographic record
Abstract
BACKGROUND: The Physiological Cost Index (PCI) was introduced by MacGregor to estimate the energy cost in walking of healthy people, also it has been reported for persons with lower limb amputation, walking with prosthesis. OBJECTIVE: To assess energy cost and walking speed in two level lower limb amputation: transfemoral and transtibial amputation and to determine if the age and prosthetic walking supported with walking aids have impact on energy cost and walking speed. METHODS: A prospective cross sectional study was performed in two level lower limb amputees with no vascular disease who were rehabilitated at the Department of Prosthetics and Orthotics at the University Clinical Center of Kosovo. The Physiological Cost Index (PCI) was assessed by five minutes of continuous indoor walking at Comfort Walking Speed (CWS). RESULTS: Eighty three lower limb amputees were recruited. It is shown relevant impact of level of amputation in PCI (t=6.8, p<0.001) and CWS (T=487, p<0.001). The great influence of using crutches during prosthetic walking in PCI (ANOVA F= 39.5 P < 0.001) and CWS (ANOVA F=32.01, P <0.001) has been shown by One Way ANOVA test. The correlation coefficient (R) showed a significant correlation of age with PCI and CWS in both groups of amputation. CONCLUSIONS: Walking with transfemoral prosthesis or using walking aids during prosthetic ambulation is matched with higher cost of energy and slower walking speed. Advanced age was shown with high impact on PCI and CWS in both groups of amputees.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".