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Record W2762782201 · doi:10.3233/wor-172614

Task intensity influences upper limb and torso kinematics during two common overhead Functional Capacity Evaluation tasks

2017· article· en· W2762782201 on OpenAlexaff
Angelica E. Lang, Clark R. Dickerson

Bibliographic record

VenueWork · 2017
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of WaterlooUniversity of Saskatchewan
Fundersnot available
KeywordsTorsoKinematicsLift (data mining)WristWork (physics)MathematicsComputer scienceSimulationPhysical medicine and rehabilitationEngineeringMedicineAnatomyPhysics

Abstract

fetched live from OpenAlex

BACKGROUND: The Functional Capacity Evaluation (FCE) is a tool used in the return-to-work process to guide treatment and decision making. Individual abilities and maximum capacity can be determined through visual observations of changes in mechanics as intensity increases. OBJECTIVE: The purpose of this study was to determine kinematic differences between sexes and intensity levels of two common FCE tasks to establish normative behaviours. METHODS: Upper limb and torso kinematics were collected from 30 participants as they performed the overhead lift and overhead work FCE tasks. Mean, maximum, and minimum values were calculated for clinically relevant joint angles. Mean and maximum segment velocity was also calculated and each variable was tested with a mixed model ANOVA. RESULTS: During the overhead lift task, maximum torso flexion and maximum torso extension increased from the lightest to the heaviest load. Humeral flexion angle at the beginning of the lift and wrist ulnar deviation also increased with load. Torso extension, humeral flexion and axial rotation, and wrist extension all increased with time during the overhead work task. CONCLUSIONS: Increasing intensity during the overhead tasks influenced kinematic variables. These observable changes can be used by evaluators to more reliably determine safe maximum capacities for each patient and identify compensatory actions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.013
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.038
GPT teacher head0.321
Teacher spread0.283 · 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 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

Citations9
Published2017
Admission routes1
Has abstractyes

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