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Record W109333748 · doi:10.2340/165019779527137143

Comparison of the Jamar dynamometer and the Martin vigorimeter for grip strength measurements in a healthy elderly population.

2020· article· en· W109333748 on OpenAlexaff
J. Desrosiers, Réjean Hébert, Gina Bravo, Élisabeth Dutil

Bibliographic record

VenueJournal of Rehabilitation Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsHôpital d'Youville de Sherbrooke
Fundersnot available
KeywordsGrip strengthDynamometerAnthropometryPhysical medicine and rehabilitationHand strengthMuscle strengthPopulationPhysical therapyMedicineEngineering

Abstract

fetched live from OpenAlex

Grip strength is considered to be a good indicator of upper limb strength. The Jamar dynamometer and the Martin vigorimeter are two instruments frequently used to assess grip strength in clinical and research settings. The purpose of this study was to compare these instruments for assessing grip strength in 360 people aged 60 to 94 years, randomly selected from the electoral list. Anthropometric data were also collected. Data analyses were done using the maximum value on 3 trials with each instrument. Although the Martin vigorimeter is a pressure measure implying a dynamic movement as opposed to the static strength measure of the Jamar dynamometer, results indicate a very high correlation between the two measures. Grip strength measured by the Jamar dynamometer is even more dependent on hand anthropometry than measurements with the Martin vigorimeter.

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)0.006
metaresearch head score (Gemma)0.012
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 score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.090
GPT teacher head0.411
Teacher spread0.321 · 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

Citations116
Published2020
Admission routes1
Has abstractyes

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