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Record W2330728100 · doi:10.2340/16501977-1827

Learning effects of repetitive administration of the Southampton Hand Assessment Procedure in novice prosthetic users

2014· article· en· W2330728100 on OpenAlexaff
Ecaterina Vasluian, Raoul M. Bongers, Heleen A. Reinders‐Messelink, Johannes G. M. Burgerhof, Pieter U. Dijkstra, Corry K. van der Sluis

Bibliographic record

VenueJournal of Rehabilitation Medicine · 2014
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsCentre for Movement Disorders
FundersZonMw
KeywordsSession (web analytics)Task (project management)Reliability (semiconductor)Computer sciencePhysical medicine and rehabilitationPsychologyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.234
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations45
Published2014
Admission routes1
Has abstractyes

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