Normative Data of Trunk Muscle Endurance for Male Long-Distance Runners of Vadodara: An Observational Study
Bibliographic record
Abstract
Introduction: Over last two decades, Importance of trunk core muscle exercise has gain major interest inclinical as well as in profession athletes. Trunk muscle endurance and strength are routinely used measuresin clinical evaluation, but lack of normative data is a limitation for measuring physical function of trunk inathletes.Aim: Aim of the study is to find out normative data of trunk muscle endurance for male long-distancerunners of Vadodara.Objectives: To Establish normative data of Trunk flexors, extensors and side flexors endurance usingMcGill’s Endurance Test.Methodology: 80 male participants of 18-35 years were taken randomly from various sports ground ofVadodara. Study Design: Cross sectional Observation study. Outcome Measures: McGill’s Endurance TestProcedure: Participants was selected based on Inclusion Criteria and McGill’s Endurance test was used toexamine trunk muscle endurance. These tests consist of four positions: 1) Trunk anterior flexor test, 2) Trunkposterior extensor test, 3) Latera side plank (Right) and 4) Lateral side plank (left). Statistical analysis:Descriptive statistics were used to summarise the data of all participants. Endurance levels between 25 and50 consider low, between 50 and 75 consider medium and between 75 and 100 consider high.Result: The mean value of trunk anterior flexor test was 148 sec, Trunk posterior extensor test was 108 sec,Lateral side plank (Right) was 83.2 sec and Lateral side plank (Left) was 81.5 sec.Conclusion: This study was done to find out the normative data for endurance of trunk muscle in longdistance runners of 18–35 age group. The reference value of trunk muscle endurance found in this studycan be helpful for the quantitative evaluation, Rehabilitation programme and for the research purpose in thelong-distance runners.
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 | 0.002 |
| 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".