Evaluation of the Training Session in Elite Paralympic Powerlifting Athletes Based on Biomechanical and Thermal Indicators
Bibliographic record
Abstract
Background: Paralympic powerlifting (PP) is performed on a bench press, aiming to lift as much weight as possible in a single repetition. Purpose: To evaluate thermal asymmetry and dynamic force parameters with 45 and 80% 1 Repetition Maximum (1 RM) in PP athletes. Methods: Twelve elite PP male athletes were evaluated before and after a training session regarding skin temperature (thermography) and dynamic force indicators (Average Propulsive Velocity-MPV, Maximum Velocity-VMax, and Power). The training consisted of five series of five repetitions (5 × 5) with 80% 1 RM. The force indicators and dynamics before and after (45% 1 RM) were evaluated in series “1” and “5” with 80% 1 RM. Results: The temperature did not present asymmetry, and there were differences between the moment before and after. In MPV, Vmax, and Power, with 45% 1 RM, there were differences both in asymmetry and in moments (p < 0.005). With 80% 1 RM, asymmetry was observed, but no differences between moments (p < 0.005). Conclusion: No thermal asymmetry was observed. There were reductions in MVP and VMax at 45 and 80% 1 RM but without significant differences between time points (before and after). However, there was asymmetry in the moments before and after within a safety standard, where Paralympic powerlifting was safe in terms of asymmetries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".