Adaptation of Anaerobic Field-Based Tests for Wheelchair Basketball Athletes
Bibliographic record
Abstract
Purpose: The aim of this study was to propose field-based tests to estimate the anaerobic power of wheelchair basketball athletes. Methods: Eleven lower class wheelchair basketball players performed the Wingate test (WT) and two field-based tests (repeated sprints) of 15 (S-15) and 20 (S-20) meters. The WT provides data in Watts (W). The S-15 and S-20 are recorded in seconds and converted to W using the Running-based Anaerobic Sprint Test (RAST) equation. The participants also completed other field-based tests, such as right and left handgrip strength (HGS) tests and the medicine ball chest pass test. In addition, body mass and height were measured, and the body composition was estimated. The field-based tests and anthropometric measures were used to estimate WT peak power (PP) and mean power (MP) using multiple linear regressions. Results: The field-based tests underestimated the anaerobic power measured with the WT (in W). However, a linear regression model based on S-15 PP, right HGS, height, and body mass explained 76% (P= .040) of the WT PP variance. Another model based on S-15 MP and right HGS explained 72% (P= .006) of the WT MP variance. Both models had excellent reliability (ICC > 0.90). Conclusion: WT PP can be estimated using S-15 PP (W), right HGS, height, and body mass. The WT MP is predicted using S-15 MP (W) and right HGS. Therefore, a combination of field-based tests and anthropometric measures seem to be appropriate to determine anaerobic power of lower class wheelchair basketball athletes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".