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Record W4401118198 · doi:10.47391/jpma.10743

Evaluation of lumbar core stability among club level cricket bowlers: a cross-sectional study

2024· article· en· W4401118198 on OpenAlexaboutno aff
Hafsa, Sabahat Butt, Amna Imran, Moazzam Ali, Zara Khalid, Hafiz Ali Bin Asim

Bibliographic record

VenueJournal of the Pakistan Medical Association · 2024
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsnot available
Fundersnot available
KeywordsCricketCross-sectional studyCore stabilityCore (optical fiber)MedicinePhysical therapyPhysical medicine and rehabilitationEngineeringBiologyTelecommunications

Abstract

fetched live from OpenAlex

Objectives: To evaluate the lumbar core stability in club-level cricket bowlers. METHODS: This descriptive cross-sectional study was conducted in the twin cities of Rawalpindi and Islamabad in Pakistan from July 15 to December 10, 2022, after approval from the ethics review board Foundation University Medical College, Islamabad, and comprised male, club-level, hard-ball cricket bowlers aged 18-24 years. Data was collected through a self-structured demographic sheet, and core stability was assessed using McGill Torso Muscle Endurance Test Battery. Data was analysed using SPSS 21. RESULTS: There were 296 male subjects with a mean age of 20.1±1.77 years. Of them, 90(30.4%) bowlers had good lumbar flexion-to-extension ratio and 206(69.6%) had poor ratio. Lateral endurance test of right-to-left side-bridge ratio showed 71(24%) players in the good category, and 225(76%) in the poor category. The ratio of right lateral endurance to lumbar extensor was good in 55(18.6%) and poor in 241(81.4%) subjects. The ratio of left lateral endurance to lumbar extensor endurance was good in 40(13.5%) players and poor in 256(86.5%). CONCLUSIONS: Lumbar core stability was found to be quite poor among club-level cricket bowlers of Rawalpindi and Islamabad.

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)0.028
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.006
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 integrity0.0000.001
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 head0.095
GPT teacher head0.430
Teacher spread0.335 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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