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Record W4362635966 · doi:10.1016/j.jht.2022.12.003

How controlled motion alters the biophysical properties of musculoskeletal tissue architecture

2023· article· en· W4362635966 on OpenAlexaff
Maureen A. Hardy, Lynne M. Feehan, Georgia Savvides, Jason Wong

Bibliographic record

VenueJournal of Hand Therapy · 2023
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsUniversity of British Columbia
FundersInnovate UK
KeywordsRegeneration (biology)Motion (physics)Computer scienceMovement (music)NeuroscienceBiologyArtificial intelligenceCell biologyPhysics

Abstract

fetched live from OpenAlex

INTRODUCTION: Movement is fundamental to the normal behaviour of the hand, not only for day-to-day activity, but also for fundamental processes like development, tissue homeostasis and repair. Controlled motion is a concept that hand therapists apply to their patients daily for functional gains, yet the scientific understanding of how this works is poorly understood. PURPOSE OF THE ARTICLE: To review the biology of the tissues in the hand that respond to movement and provide a basic science understanding of how it can be manipulated to facilitate better functionThe review outlines the concept of controlled motion and actions across the scales of tissue architecture, highlighting the the role of movement forces in tissue development, homeostasis and repair. The biophysical behaviour of mechanosensitve tissues of the hand such as skin, tendon, bone and cartilage are discussed. CONCLUSION: Controlled motion during early healing is a form of controlled stress and can be harnessed to generate appropriate reparative tissues. Understanding the temporal and spatial biology of tissue repair allows therapists to tailor therapies that allow optimal recovery based around progressive biophysical stimuli by movement.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.156

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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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 designBench or experimental
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

Citations5
Published2023
Admission routes1
Has abstractyes

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