Learning effects of repetitive administration of the Southampton Hand Assessment Procedure in novice prosthetic users
Bibliographic record
Abstract
OBJECTIVE: The Southampton Hand Assessment Procedure (SHAP) evaluates the functionality of normal, injured or prosthetic hands. The aim was to evaluate the learning effects of SHAP tasks and the appropriateness of the time limits applied per task in novice prosthetic users. METHODS: Right-handed unimpaired volunteers (n = 24, mean age 21.8 years) completed 8 SHAP sessions over 5 consecutive days using a prosthetic simulator. The execution times of SHAP tasks were transformed into 6 prehensile patterns, the functionality profile, and the index of function, a general functionality score. Learning effects in task times were analysed using multilevel analysis. RESULTS: Learning effects occurred in all SHAP tasks. Tasks, sex, sessions, tasks-sessions interaction, and the first session of the day contributed (p < 0.01) to the execution times. Tasks were performed more slowly by females and more slowly on the first session of the day. In several tasks time limits were exceeded by > 25% of participants in at least the first 3 sessions, which affected the calculation of the functionality profile and index of function scores. CONCLUSION: The learning effects of SHAP in novice prosthetic users require consideration when conducting a reliability study. SHAP scores in novice prosthetic-hand users are confounded by learning effects and exceeded time limits.
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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 | 0.002 | 0.020 |
| 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 | 0.001 |
| 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".