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Record W1999693968 · doi:10.1109/memb.2003.1195691

Change-in-support reactions for balance recovery

2003· review· en· W1999693968 on OpenAlexaff
Brian E. Maki, William E. McIlroy, Geoff Fernie

Bibliographic record

VenueIEEE Engineering in Medicine and Biology Magazine · 2003
Typereview
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of GuelphHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsBalance (ability)Physical medicine and rehabilitationPerspective (graphical)Fall preventionCognitionTrajectoryGround reaction forcePsychologyPhysical therapyPoison controlComputer scienceInjury preventionMedicineNeuroscienceArtificial intelligenceMedical emergencyKinematics

Abstract

fetched live from OpenAlex

The authors provide an overview of their own recent research in "change-in-support" reactions covering control mechanisms, age-related changes, and implications for fall prevention. Compensatory stepping and grasping are critical reactions for preventing falls. These reactions are much more rapid than volitional limb movements and can be very effective in decelerating the center of mass (COM) motion induced by sudden unpredictable perturbation, despite environmental constraints on limb trajectory and additional demands imposed by ongoing physical or cognitive activity. However, even healthy older adults experience difficulty in controlling these reactions, and they appear to have particular problems in controlling lateral stability and lateral leg movement. These problems may be particularly relevant to the problem of hip fractures, which are most likely to occur as a result of a lateral fall. Older adults also appear to be more reliant on grasping reactions than young adults, but they are less able to execute these reactions rapidly. From a clinical perspective, it is important to assess compensatory stepping and grasping. Such tests could be used as a screening tool to identify high-risk individuals and could also serve to pinpoint specific control problems to target for balance training or other intervention. More effective use of stepping and grasping reactions can be promoted through improved design of footwear, mobility aids, handrails, and grab-bars.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.915
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.120
GPT teacher head0.431
Teacher spread0.311 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations153
Published2003
Admission routes1
Has abstractyes

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