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Record W4206246064 · doi:10.1080/00140139.2022.2030806

A design tool to estimate maximum acceptable manual arm forces for above-shoulder work

2022· article· en· W4206246064 on OpenAlexaff
David Rempel, Jim R. Potvin

Bibliographic record

VenueErgonomics · 2022
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsMcMaster University
FundersNational Institute for Occupational Safety and Health
KeywordsWork (physics)Shoulder jointMedicineAnterior shoulderPhysical therapyPhysical medicine and rehabilitationOrthodonticsSurgeryEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

AFF: arm force field; AP: anterior/posterior; DC: duty cycle; GH: glenohumeral angle; HT: humerothoracic angle; LM: lateral/medial; MAE: maximum acceptable effort; MAF: maximum acceptable force; MAS: manual arm strength; MVC: maximum voluntary contraction; N: newton; OCRA: occupational repetitive action; R: reach distance; RMS: root means square; RULA: rapid upper limb assessment; SF: scale factor; SI: superior/inferior; ST: scapulothoracic angle; T: thoracicKEY POINTSA new design tool is introduced that estimates maximum acceptable hand forces for specific locations above the shoulder.This above-shoulder tool is based on a 50th percentile female anthropometry and 25th percentile female manual arm strength.These base strengths are multiplied by scaling factors that adjust for subacromial impingement and fatigue.The tool was shown to be robust based on sensitivity analysis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.041
GPT teacher head0.342
Teacher spread0.301 · 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 designNot applicable
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

Citations7
Published2022
Admission routes1
Has abstractyes

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