Evaluation of lumbar core stability among club level cricket bowlers: a cross-sectional study
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".