Biomechanical analyses of the performance of Paralympians
Bibliographic record
Abstract
A biomechanical analysis of sports performance provides an objective method of determining performance of a particular sporting technique. In particular, it aims to add to the understanding of the mechanisms influencing performance, characterization of athletes and provide insights into injury predisposition. While the performance in sport of able-bodied athletes is well recognized in the literature, less information and understanding are known on the complexity, constraints and demands placed on the body of an individual with a disability. This article provides a dialogue that outlines scientific issues of the performance analysis of multi-level athletes with a disability, including Paralympians. Four integrated themes are explored, the first of which focuses on how biomechanics can contribute to the understanding of sports performance in athletes with a disability and how it may be used as an evidence-based tool. This latter point questions the potential for a possible cultural shift led by the emergence of user-friendly instruments. The second theme briefly discusses the role of reliability of sports performance and addresses the debate of two-dimensional and three-dimensional analyses. The third theme addresses key biomechanical parameters and provides guidance to clinicians and coaches on the approaches adopted using the biomechanical/sports performance analysis for an athlete with a disability starting out, to the emerging and elite Paralympian. For completeness of this discourse, the final theme is based on the controversial issues on the role of assisted devices, and the inclusion of Paralympians into able-bodied sport. All combined, this dialogue highlights the intricate relationship between biomechanics and training of individuals with a disability. Furthermore, it illustrates the complexity of modern training of athletes, which can only lead to a better appreciation of the performances to be delivered in the London 2012 Paralympic Games.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".