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Record W4394221432 · doi:10.6084/m9.figshare.19915162

IMPACT OF CORE FITNESS ON BALANCE PERFORMANCE IN THE ELDERLY

2022· dataset· en· W4394221432 on OpenAlexaff
Yuan Xu

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsCore (optical fiber)Balance (ability)GerontologyComputer sciencePhysical medicine and rehabilitationMedicineTelecommunications

Abstract

fetched live from OpenAlex

ABSTRACT Introduction Relevant monitoring data show that falls have become the leading cause of death in adults over 65 years old, especially among elderly people who have no exercise habits. Physiological function decline caused by the aging process can be slowed with specific training. It is believed that exercises focusing on the core muscles can benefit balance ability among the elderly. Objective The paper explores how core muscle training impacts balance performance in the elderly. Methods The article randomly divides elderly volunteers (n=24) into two groups. The experimental group received specific core physical conditioning, and the control group received no intervention. The physical quality indicators of both groups were compared and statistically analyzed after the experiment. Results The physical fitness indicators (weight, aerobic endurance, static balance ability) in the groups differed (P<0.05). Conclusion Core training can improve the elderly´s functional physical ability and static balance capacity. Evidence level II; Therapeutic Studies - Investigating the results.

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)0.006
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.062
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0040.007
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0620.007

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 head0.074
GPT teacher head0.392
Teacher spread0.317 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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