Influence of upper-body exercise order on hormonal responses in trained men
Bibliographic record
Abstract
The aim of this study was to examine acute hormonal responses after different sequences of an upper-body resistance-exercise session. Twenty men completed 2 sessions (3 sets; 70% 1-repetition maximum; 2 min passive rest between sets) of the same exercises in opposite sequences (larger to smaller vs. smaller to larger muscle-group exercises). Total testosterone (TT), free testosterone (FT), testosterone/cortisol (T/C) ratio, sex-hormone-binding globulin (SHBG), growth hormone (GH), and cortisol (C) concentrations were measured before and immediately after each sequence. The results indicate that the GH concentration increased after both sessions, but the increase was significantly greater (p < 0.05) after the sequence in which larger muscle-group exercises were performed prior to the smaller muscle-group exercises. No differences were observed between sessions for TT, FT, SHBG, C, or the T/C ratio at baseline or immediately after resistance exercise. These results indicate that performing larger muscle-group exercises first in an upper-body resistance-exercise session leads to a significantly greater GH response. This may have been due to the significantly greater exercise volume accomplished. In summary, the findings of this investigation support the common prescriptive recommendation to perform larger-muscle group exercises first during a resistance-exercise session.
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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) | 0.002 | 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".