Para Powerlifting Performance: A Systematic Review
Bibliographic record
Abstract
This research aimed to conduct a systematic review of para powerlifting strength performance. The searches were conducted in three electronic databases: PubMed, Scopus, and SPORTDiscus. Intervention studies related to para powerlifting performance were included. The main information was extracted systematically, based on criteria established by the authors. The data on study design, sample size, participant's characteristics (e. g. type of disability, sex, age, body weight, and height), training experience, assessment tools, physical performance criteria, and force-related outcomes were extracted and analyzed. The studies (n=9) describe factors related to biomechanics and performance. Outcomes revealed that the one-repetition maximum test is used as load prescription and that para powerlifting should work at high speeds and higher loads. Regarding technique, grip width with 1.5 biacromial distance provides a good lift and partial amplitude training as an alternative to training. There are no differences in total load and movement quality in the lumbar arched technique compared with the flat technique. As a monitoring method, repetitions in reserve scale was used for submaximal loads. Finally, our outcomes and discussion indicated strategies and techniques that can be used by para powerlifting coaches.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".