Changes in muscle cross-sectional area during two menstrual cycles may not be exclusively attributed to resistance training
Bibliographic record
Abstract
This study investigated the impact of menstrual cycle (MC) phases and resistance training (RT) on muscle cross-sectional area (CSA) in two MCs utilizing a within-subject design. Twenty women with regular MCs had their legs randomly allocated to either the control (CON) or RT condition, which included 16 training sessions over two MCs. CSA, estradiol (E2), and progesterone (P4) were assessed during the menstruation (M), ovulation (O), and luteal (L) phases in the first (M1, O1, L1) and second (M2, O2, L2) MCs and at the beginning of the third MC (M3). P4 values were significantly higher during the luteal phase than during menstruation ( P < 0.0001) and ovulation ( P < 0.0001). No significant differences in E2 concentrations were observed between the MC phases ( P = 0.08). For the RT condition, the CSA showed significant increases at O2, L2, and M3 compared to baseline (M1) (all P < 0.0001). No significant changes were observed for the CON condition during the two MCs ( P > 0.05). However, RT condition showed a significant change in average CSA across two MCs. Additionally, individual analyses revealed that 19 participants showed variation in CSA above or below the minimum detectable difference during the two MCs. These findings suggest that changes in muscle CSA observed during two MCs may not be exclusively attributed to RT.
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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) | 0.000 | 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".