Classic Powerlifting Performance: A Systematic Review
Bibliographic record
Abstract
Ferland, PM and Comtois, AS. Classic powerlifting performance: A systematic review. J Strength Cond Res XX(X): 000-000, 2019-The purpose of this study was to review all scientific publications related to able-body drug-tested classic powerlifting performance since January 1, 2012, and to regroup them into a brief narrative review. Three electronic databases were systematically searched in August 2018 using the wildcard: powerlift*. A manual search was performed from the reference list of all retained articles. The search and selection strategy permitted to gather a total of 16 scientific articles published in peer-reviewed journals. Results show that practitioners should prioritize a low-bar squat and a wide grip bench press because they generally contribute to moving greater loads, bring more attention to preventing injuries, since a fair amount of powerlifters seem to train injured and prioritize a hypertrophy-power-strength model when prescribing 3 times a week daily undulating periodization on nonconsecutive days for squat and bench. Practitioners could also introduce respiratory muscle training, use daily 1 repetition maximum training combined with down sets on experienced athletes and use a rate of perceived exertion scale based on repetitions in reserve combined with an individual velocity profile when prescribing intensity. Before competition, powerlifters seem to taper in this order: the deadlift, the squat, and lastly the bench press. The Slingshot does help to move more weight because it helps to generate more inertia, but it also deactivates the triceps. Finally, the present work was limited by the present literature but could serve as a reference in the field of powerlifting. Further research should include more details about the circumstances under which they were conducted.
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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| 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 | 0.000 |
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".