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The Response of the Shoulder Complex to Repetitive Work: Implications for Workplace Design

2015· review· en· W2130893716 on OpenAlexaff
Alison C. McDonald, Peter J. Keir

Bibliographic record

VenueCritical Reviews in Biomedical Engineering · 2015
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTask (project management)KinematicsWork (physics)Muscular fatiguePhysical medicine and rehabilitationComputer scienceAdaptation (eye)Selection (genetic algorithm)PsychologyEngineeringMedicineMechanical engineeringArtificial intelligenceSystems engineeringNeuroscience

Abstract

fetched live from OpenAlex

The shoulder complex has multiple degrees of freedom and muscular geometry that make it possible to complete tasks with many different kinematic and muscular strategies. Substantial research has investigated the effects of workplace factors (posture and task design) on the shoulder complex. The interactive relationships between workplace factors, however, make it challenging to synthesize the literature to make decisive conclusions regarding the impact of repetitive work. This review summarizes a broad selection of the literature examining the effects of repetitive work on the shoulder complex with respect to kinematic and muscular adaptation strategies to maintain task performance with muscular fatigue. The implications of repetitive work and workplace design on the shoulder complex are discussed.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.141
GPT teacher head0.444
Teacher spread0.303 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2015
Admission routes1
Has abstractyes

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