Adapting Isokinetic Dynamometry for Individuals with Transtibial Amputations
Bibliographic record
Abstract
Transtibial amputations impact one’s ability to perform activities of daily living. Continuous load bearing on the intact limb during ambulation and standing can lead to strength asymmetries in the lower limbs. Objective assessment of strength asymmetries in lower extremity muscles is critical as transtibial amputees are prone to several secondary conditionsstemming from these musculoskeletal imbalances. Isokinetic dynamometry has been used to safely evaluate muscle asymmetries, but testing is usually performed using the participant’sown prosthesis which can vary in available range of motion and suspension method. Furthermore, this methodology excludes those who are not prosthesis users. The purpose of this research was to design, build and test a transtibial adapter for dynamometry that can be used on the residual limb with or without a prosthesis for objective assessment of leg strength. Clinical feedback was sought from one transtibial amputee regarding the usability and comfort of the adapter while performing an isokinetic knee extension/flexion task. The participant was capable of completing the knee contractions without any reported pain or discomfort, suggesting that our prototype may be an option to adapt dynamometry for this population. Further research with the prototype with a larger sample and more contraction conditions is needed to further assess whether the design presented is a viable option to adapt dynamometry for transtibial 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 | 0.001 | 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) | 0.002 | 0.001 |
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".