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Record W4387579762 · doi:10.1007/s42600-023-00315-6

Age-related gait adaptations of ground reaction forces

2023· article· en· W4387579762 on OpenAlexaff
Rafael Reimann Baptista, Mauricio Delgado, Gustavo Sandri Heidner, Marcus Fraga Vieira

Bibliographic record

VenueResearch on Biomedical Engineering · 2023
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Calgary
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsGround reaction forceGaitGait cyclePhysical medicine and rehabilitationAge groupsForce platformMedicinePsychologyPhysical therapyDemographyPhysicsKinematics

Abstract

fetched live from OpenAlex

The purpose of this study was to compare gait ground reaction forces (GRF) between children, adults, and older adults. Twenty-five children (6.13 ± 1.86 years), 30 adults (34.37 ± 5.67 years), and 33 older adults (70.45 ± 6.92 years) walked on force platforms. Velocity, GRF, and impulses were calculated. Gait velocity was higher in children compared to older adults ( p = 0.031). Gait velocity had an inverse effect in GRF parameters across all groups. The vertical peak of force appeared at 22% of the gait cycle in children, at 27.3% in adults, and at 25.7% in older adults. The anterior–posterior force peak appeared at 14.8% of the gait cycle in children, at 17.1% in adults, and at 16.5% in older adults. Ground reaction forces were higher in children ( p < 0.05) and similar between adults and older adults ( p > 0.05). Gait speed was higher in children compared to older adults, only ( p = 0.031). Gait ground reaction forces and impulses were higher in children and similar between adults and older adults walking at a self-selected velocity.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.103
GPT teacher head0.445
Teacher spread0.342 · 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 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

Citations2
Published2023
Admission routes1
Has abstractno

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