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Record W2124278820 · doi:10.1002/mus.20750

Normative values and the effects of age, gender, and handedness on the Moberg Pick‐Up Test

2007· article· en· W2124278820 on OpenAlexaff
Nasim Amirjani, Nigel Ashworth, Tessa Gordon, David C. Edwards, K. Ming Chan

Bibliographic record

VenueMuscle & Nerve · 2007
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWilcoxon signed-rank testNormativeTest (biology)Mann–Whitney U testPhysical medicine and rehabilitationMedicinePsychologyAudiologyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

The Moberg Pick-Up Test is a standardized test for assessing hand dexterity. Although reduction of sensation in the hand occurs with aging, the effect of age on a subject's performance of the Moberg Pick-Up Test has not been examined. The primary goal of this study was to examine the impact of aging and, secondarily, the impact of gender and handedness, on performance of the Moberg Pick-Up Test in 116 healthy subjects. The average time to complete each of the four subsets of the test was analyzed using the Kruskal-Wallis, Mann-Whitney U, and Wilcoxon signed-rank tests. The results show that hand dexterity of the subjects was significantly affected by age, with young subjects being the fastest and elderly subjects the slowest. Women accomplished the test faster than men, and task performance with the dominant hand was faster than with the non-dominant hand. Use of normative values established based on age and gender is a valuable objective tool to gauge hand function in patients with different neurologic disorders.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations97
Published2007
Admission routes1
Has abstractyes

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