EVIDENCE ON PROSTHETIC FEET WITH ACTIVE DORSIFLEXION FEATURE, PASSIVE MICROPROCESSOR CONTROL AND ACTIVE ANKLE POWER GENERATION: A MINI LITERATURE REVIEW
Bibliographic record
Abstract
This paper reviewed 11 publications on non-MP controlled ankles with active dorsiflexion feature, 15 publications on passive MP controlled ankles, and 12 publications on powered MP controlled ankle-foot mechanisms. Methodological quality of publications was low to moderate. The evidence found was mostly biomechanical and generated in gait lab studies. Non-MP ankles may increase toe clearance and reduce braking forces during level walking, thus supporting propulsion with increase in walking speed. Passive MP controlled ankles may also increase toe clearance and reduce the likelihood of stumbling over an unseen obstacle. They may reduce energy expenditure during level walking and facilitate slope and stair ambulation. Non-MP and passive MP controlled ankles have been also been shown to reduce residual limb-socket interface pressures. Powered ankles may increase walking speed to the level of and decrease energy expenditure to be no longer significantly different from that of able-bodied individuals. Also, at higher walking speeds the sound knee loading may be reduced by up to 15-20%. However, it remains unclear to what extent the gait lab results for all advanced ankle-foot mechanisms can be transferred to real-life benefits in the free-living environment. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/30450/23055 How to cite: Kannenberg A. Evidence On Prosthetic Feet With Active Dorsiflexion Feature, Passive Microprocessor Control And Active Ankle Power Generation: A Mini Literature Review. Canadian Prosthetics & Orthotics Journal, Volume 1, Issue 1, No 6, 2018, DOI: https://doi.org/10.33137/cpoj.v1i1.30450
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".