Does increasing the resistance-training volume lead to greater gains? The effects of weekly set progressions on muscular adaptations in females
Bibliographic record
Abstract
We investigated the effect of increasing the number of sets per week every fortnight versus performing a constant set volume on muscular adaptations over 12 weeks. Thirty females (RT experience 2.1 ± 1.0 years) were randomly assigned to a constant group (CG, n = 9) that performed 22 sets per week, a two-set progression group (2SG, n = 11), or a four-set progression group (4SG, n = 10). Forty-five degree leg press one-repetition maximum (1RM), vastus lateralis cross-sectional area (VL-CSA), and the sum of proximal, middle and distal lateral thigh muscle thickness (∑MT) were assessed at baseline and after the intervention. We observed that the 4SG and 2SG conditions showed greater improvements in 1RM than the CG (p < 0.001, p = 0.032, respectively), with no differences between 4SG and 2SG (p = 0.118). Regarding VL-CSA, the 4SG group showed greater increases than the CG (p = 0.029) but not than the 2SG (p = 0.263), whereas no differences between the 2SG and CG (p = 0.443) were observed. There were no differences between groups for ∑MT (p = 0.783). While all groups demonstrated improvements in the measured outcomes, our findings suggest that increasing weekly sets may offer additional benefits for 1RM and vastus lateralis cross-sectional area. However, no additional benefits were observed for ∑MT.
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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) | 0.003 | 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".