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Record W3189209710 · doi:10.36408/mhjcm.v8i2.590

Perbedaan Efektivitas Latihan Core dengan Swissball dan Resistance Band terhadap Peningkatan Endurance Otot Core Remaja Obesitas

2021· article· id· W3189209710 on OpenAlexaboutno aff
Iceu Helmina Hurriawati, Rudy Handoyo, Hari Peni Julianti

Bibliographic record

VenueMedica Hospitalia Journal of Clinical Medicine · 2021
Typearticle
Languageid
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

Latar belakang : Otot core berperan penting dalam aktivitas sehari-hari. Individu yang mengalami obesitas cenderung memiliki endurance otot core yang rendah. Remaja adalah masa dimana seseorang banyak melakukan aktivitas fisik seperti naik turun tangga di sekolah dan masa kritis pertumbuhan untuk menjdi dewasa. Remaja obesitas dengan endurance otot core yang rendah berisiko mengalami cedera otot saat aktivitas atau nyeri punggung bawah pada jangka panjang. Endurance otot core dapat ditingkatkan dengan latihan core menggunakan swissball atau resistance band. Tujuan : membuktikan perbedaan efektivitas latihan core dengan swissball dan resistance band terhadap endurance otot core remaja obesitas. Metode : Penelitian ini merupakan randomized controlled trial. Sebanyak 36 remaja obesitas yang memenuhi kriteria inklusi dan eksklusi dirandomisasi dan dibagi kedalam dua kelompok latihan core dengan swissball (n=18) dan resistance band (n=18). Kelompok latihan core dengan swissball dan resistance band masing-masing menjalani latihan selama 6 minggu dengan frekuensi 3 kali per minggu, dengan durasi latihan 40 menit tiap sesi. Endurance otot core dinilai dengan McGill Core Endurance test. Kesimpulan : Latihan core dengan resistance band dapat meningkatkan endurance otot core remaja obesitas lebih tinggi dibandingakan dengan latihan core dengan menggunakan swissball.

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)0.013
metaresearch head score (Gemma)0.046
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.046
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.008
Insufficient payload (model declined to judge)0.0130.000

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 head0.182
GPT teacher head0.520
Teacher spread0.339 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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