Associations between partial foot amputation level, gait parameters, and minimum impairment criteria in para-sport: A research study protocol
Bibliographic record
Abstract
Altered biomechanics due to amputation can contribute to substantial limitations, influencing sporting activities. Individuals with lower extremity amputations or congenital lower limb deficiency are encouraged to participate in para-sports. However, to compete in Paralympic sports, the candidate must have an impairment that results in lower extremity loss of function and meets or exceeds the sport's minimum impairment criteria (MIC). This review will focus on the MIC for competitive wheelchair tennis. Limb deficiency is known as one of the MIC used to regulate participation in competitive para-sports since it impacts gait, kinematics, and biomechanics of both the upper and lower body. Notwithstanding, it is questionable whether the MIC concerning limb deficiency is set at the correct level for determining eligibility for participating in Paralympic sports. This study aims to provide an overview of the evidence examining the impact of different partial foot amputation (PFA) levels on gait as a proxy for sporting performance. This scoping review will be based on a 6-step methodological framework and Preferred Reporting Items for Systematic Reviews and Meta-Analysis, extension for scoping reviews (PRISMA-ScR). Studies will be selected from PubMed, Embase, CINAHL, and SPORTDiscus. Two authors will screen the titles/abstracts independently. Selected studies will be scrutinised, and the same authors will extract data. Findings will be relevant to informing the evidence-based development of MIC for lower limb impairment after PFA and may be extrapolated to specific Paralympic sports, including wheelchair tennis. Results will be disseminated through scientific publications and conferences to audiences interested in Paralympic sports.
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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 | 0.077 | 0.054 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.070 | 0.018 |
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".