Static and Dynamic Strength Indicators in Paralympic Power-Lifters with and without Spinal Cord Injury
Bibliographic record
Abstract
Background: In Paralympic powerlifting (PP), athletes with and without spinal cord injury (SCI) compete in the same category. Athletes with SCI may be at a disadvantage in relation to the production of muscle strength and the execution of motor techniques. Objective: To analyze the indicators force, dynamic and static, at different intensities, on performance in athletes with and without SCI. Methods: The sample was composed of two groups of PP athletes: SCI (30.57 ± 4.20 years) and other deficiencies (OD; 25.67 ± 4.52 years). Athletes performed a test of maximum isometric force (MIF), time to MIF (Time), rate of force development (RFD), impulse, variability and fatigue index (FI), dynamic tests Mean Propulsive Velocity (MPV), Maximum Velocity (Vmax) and Power. Results: There were differences in the SCI in relation to OD, 50% 1RM (p < 0.05), in relation to MPV and Vmax. There were no differences in the static force indicators. Regarding EMG, there were differences between the SCI triceps in relation to the previous deltoid (p = 0.012). Conclusion: We concluded that the static and dynamic strength indicators are similar in Paralympic powerlifting athletes with spinal cord injury and other disabilities.
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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".