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Record W2040434857 · doi:10.1152/jn.01030.2002

How Do Infants Adapt to Loading of the Limb During the Swing Phase of Stepping?

2003· article· en· W2040434857 on OpenAlexaff
Tania Lam, Claire Wolstenholme, Jaynie F. Yang

Bibliographic record

VenueJournal of Neurophysiology · 2003
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSwingPhysical medicine and rehabilitationPsychologyPhase (matter)NeuroscienceCommunicationMedicinePhysicsAcoustics

Abstract

fetched live from OpenAlex

Previous results from this laboratory have shown that human infants (<12 mo old) respond appropriately to transient changes in sensory input during stepping. We examined how infants adapted to a more enduring change in sensory input by applying load to one limb during stepping. A small weight (500-900 g) was strapped around the lower leg of infants aged 3-11 mo. Stepping with the weight on was recorded on the treadmill for a period of 0.5-3 min. The weight was then quickly detached during stepping, and the immediate response to unexpected loss of the weight recorded. Three-segment dynamic analysis of leg motion was used to estimate hip, knee, and ankle torques during swing in the sagittal plane. All infants adapted to the additional load on the leg by immediately increasing the generation of hip and knee flexor muscle torques. When the weight was removed, 7 of the 22 infants tested exhibited an after-effect (high stepping) in the first step after removal of the weight. The after-effect was manifested as an increase in toe trajectory height and hip flexion and coincided with higher hip flexor muscle torque in early swing. In an additional series of control experiments using seven infants, after-effects were shown to be unrelated to a sudden change in cutaneous input with removal of the weight. The presence of an after-effect indicates that some infants made an enduring adaptation to their stepping pattern that is revealed with the unexpected removal of the weight.

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)0.002
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 score0.063
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.033
GPT teacher head0.268
Teacher spread0.235 · 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

Citations54
Published2003
Admission routes1
Has abstractyes

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