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Record W4417304261 · doi:10.1016/j.apergo.2025.104709

An alternative 6-inch octave keyboard reduces forearm muscle activation level and improves finger postures in expert pianists with smaller hand spans

2025· article· en· W4417304261 on OpenAlexafffund
Craig Turner, Justine Pelletier, Robin Mailly, Fabien Dal Maso, Mickaël Begon, Felipe Verdugo

Bibliographic record

VenueApplied Ergonomics · 2025
Typearticle
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersFonds de recherche du Québec – Nature et technologiesSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaUniversité de Montréal
KeywordsForearmOctave (electronics)Octave bandFinger jointElectromyographyUpper limbJoint (building)

Abstract

fetched live from OpenAlex

A small hand span is a risk factor of musculoskeletal injuries in pianists. Alternatively sized keyboards are recommended to help smaller hand pianists reduce risk of injury. However, in comparison to a 6.5″ octave conventional keyboard, it is unclear if a 6.0" octave keyboard (DS6.0) reduces forearm muscle activations and no research has investigated if keyboard size impacts finger joint kinematics. The objective was to determine the effect of keyboard size and hand size on pianists' right forearm muscle activation and finger joint posture when playing large handspan chords. Smaller hand pianists exhibited 11.2-13.8 % greater finger/wrist extensor activation and more abducted/less flexed finger postures compared to larger hand pianists. Smaller hand pianists playing on a DS6.0 reduced finger/wrist extensor activation by 2.5-3.2 %. Fifth finger posture was more flexed and less abducted. Results suggest that playing on a DS6.0 might reduce the exposure to risk of injury in pianists with smaller hands.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0070.001

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.

Opus teacher head0.023
GPT teacher head0.288
Teacher spread0.266 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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