Enhancing the efficacy of the 20 m multistage shuttle run test
Bibliographic record
Abstract
OBJECTIVE: Maximal oxygen uptake (Vo(2max)) of 44 ml kg(-1) min(-1) is an accepted criterion (Vo(2CR)) below which health and fitness for young male adults may be compromised. New algorithms validated for Vo(2CR) screening using the 20 m multistage shuttle run test (20mMST) were developed. METHODS: Vo(2max) was assessed in 110 males using a stationary gas analyser in a treadmill test (TT) and in 40 of these subjects using a portable gas analyser in the 20mMST. Vo(2max) predicted from the 20mMST in 70 subjects was used for cross validation. Two equations predicting Vo(2max) during 20mMST (EQ(MST)) and TT (EQ(TT)) were developed. RESULTS: Significant energy cost variance (EC(V)) was detected between TT and 20mMST (p<0.001), correlated significantly with subject height, and was a significant predictor of Vo(2max) differences between TT and 20mMST. The r(2) of EQ(MST) was 0.92 (p<0.001). Predicted Vo(2max) values from EQ(MST) correlated with directly measured 20mMST Vo(2max) at r = 0.96 (p<0.001). ANOVA detected no mean difference (p>0.05) between predicted and measured values. Prevalence of low fitness based on Vo(2CR) was 0.37. McNemar chi(2) indicated significant differences in sensitivity (p<0.001) and specificity (p<0.05) between the original 20mMST equation (EQ(LEG)) and EQ(TT), regarding Vo(2CR) screening. Cohen's kappa demonstrated higher agreement with TT Vo(2max) for EQ(TT) (p<0.001) than EQ(LEG) (p<0.05). TT Vo(2max) correlated with the end result of both EQ(LEG) and EQ(TT) at r = 0.75 (p<0.001). Unlike EQ(TT) (p>0.05), mean predicted Vo(2max) from EQ(LEG) was significantly higher compared to TT Vo(2max) (p<0.001). CONCLUSION: These algorithms increase the efficacy of 20mMST to accurately evaluate aspects of health and fitness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".