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Record W2083298296 · doi:10.1080/00140139.2015.1013575

Development of a kinematic model to predict finger flexor tendon and subsynovial connective tissue displacement in the carpal tunnel

2015· article· en· W2083298296 on OpenAlexafffund
Aaron M. Kociolek, Peter J. Keir

Bibliographic record

VenueErgonomics · 2015
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCarpal tunnel syndromeTendonCarpal tunnelFinger jointWristAnatomyMedicineDisplacement (psychology)BiomechanicsOrthodonticsSurgeryPsychology

Abstract

fetched live from OpenAlex

Finger flexor tendinopathies and carpal tunnel syndrome are histologically characterised by non-inflammatory fibrosis of the subsynovial connective tissue (SSCT) in the carpal tunnel, which is indicative of excessive and repetitive shear forces between the finger flexor tendons and SSCT. We assessed flexor digitorum superficialis (FDS) tendon and adjacent SSCT displacements with colour Doppler ultrasound as 16 healthy participants completed long finger flexion/extension movements captured by a motion capture system. FDS tendon displacements fit a second-order regression model based on metacarpophalangeal and proximal interphalangeal joint flexion angles (R(2) = 0.92 ± 0.01). SSCT displacements were 33.6 ± 1.7% smaller than FDS tendon displacements and also fit a second-order regression model (R(2) = 0.89 ± 0.01). FDS tendon and SSCT displacement both correlated with finger joint thickness, enabling participant-specific anthropometric scaling. We propose the current regression models as an ergonomic method to determine relative displacements between the finger flexor tendons and SSCT. PRACTITIONER SUMMARY: Relative displacements between the finger flexor tendons and SSCT provide insight into gliding and friction in the carpal tunnel. Our regression models represent a move towards mechanistic-based ergonomic risk assessment of the wrist/hand. This is a natural evolution of ergonomic methods based on tendon-joint interaction.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.380

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)0.0000.000

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 head0.050
GPT teacher head0.306
Teacher spread0.255 · 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 designQualitative
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

Citations8
Published2015
Admission routes2
Has abstractyes

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