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Record W4411262396 · doi:10.56532/mjsat.v5i1.441

Evaluation of Trunk Endurance in Female Physiotherapy Students Using McGill Core Endurance Test

2025· article· en· W4411262396 on OpenAlexaboutno aff
Humaira Saffiah Najeeb, Vijayamurugan Eswaramoorthi, Azhar Khairuddin, Nurul Farihah Ismail, Nurhilyana Anuar, F. Fauzi

Bibliographic record

VenueMalaysian Journal of Science and Advanced Technology · 2025
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsTrunkCore (optical fiber)Physical therapyTest (biology)Endurance trainingMedicinePhysical medicine and rehabilitationPsychologyComputer science

Abstract

fetched live from OpenAlex

This cross-sectional study evaluates trunk endurance using the McGill Core Endurance Test among female physiotherapy students. Results indicate significantly lower endurance compared to normative values (p<0.001), suggesting implications for musculoskeletal health and professional performance. Trunk endurance, critical for maintaining posture, injury prevention, and task performance, warrants emphasis in physiotherapy training. Studies, including Hanney et al. (2016), highlight its importance for professional efficacy and health outcomes. Further research is recommended to explore targeted interventions.

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 score0.007

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 head0.035
GPT teacher head0.398
Teacher spread0.363 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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