Predicting one repetition maximum in novice males: An RPE-based bench press model
Bibliographic record
Abstract
This study aimed to estimate one repetition maximum (1RM) of healthy males based on the Rating of Perceived Exertion (RPE) in bench press movement and to provide a particular predictive equation. Seventy healthy males (Age: 24.93 ± 0.64 years; BMI: 25.04 ± 0.35 kg/m2) with no previous experience of resistance training performed 1RM of bench press with closed eyes and then chose a number to determine the intensity by RPE scale of 1-10 (CR1-10 scale). The intensity of this repetition was randomly selected based on the percentages of 1RM: 60%, 65%, 70%, 75%, 80%, 85%, 90% and 95%. A special prediction equation was provided based on a mathematical model using the weight lifted, RPE, and RPE coefficient. Moreover, the standardized testing protocol of 1RM (STP) and Brzycki equation protocol (BEP) were used to verify the validity of the RPE equation. Predictive equations were produced and cross-validated using repeated k-fold cross-validation by stepwise multiple linear regression. BEP and STP did not differ significantly from RPE-equation in predicting 1RM (p > 0.05). Based on the BEP and STP, a 1RM attempt for inexperienced males resulted in 74.20 ± 2.24 kg and 77.47 ± 2.17, respectively, while the RPE equation produced 77.86 ± 2.58 kg. There were significant results for linear regression (p = 0.001). Pearson correlation coefficients between BEP and STP with predicted 1RM were 0.99. The new method of 1RM based on RPE performed well in 1RM performance in inexperienced males, and it appeared safe, accurate, and time-effective.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".