Case report of modified Box and Blocks test with motion capture to measure prosthetic function
Bibliographic record
Abstract
This case study report demonstrates the use of motion analysis with a modification of the Box and Blocks test. The goal was to quantify observed improvements in compensatory movements and simultaneous control in a subject using different prostheses before and after targeted muscle reinnervation (TMR) surgery. This is a single case study with data collection using a body-powered prosthesis pre-TMR surgery and 6 mo postfitting with a TMR myoelectric prosthesis. The Box and Blocks test was modified for cyclical motion within a motion capture laboratory. With the TMR myoelectric prosthesis, the subject was able to simultaneously activate the hand and elbow. Task performance was slower, but there was improved elbow flexion and less trunk compensatory motion than with the body-powered prosthesis. There are several limitations to the case study because there is no direct comparison of myoelectric performance before and after TMR surgery; however, the current report presents a potential method to quantify quality of motion and compensatory movements of prosthetic users. With further study, this test procedure has the potential to be a useful outcome measure for future standardized assessments of upper-limb prosthetic function.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".