The repeated bout effect of traditional resistance exercises on running performance across 3 bouts
Bibliographic record
Abstract
; 6-repetition maximum squat, 71.7 ± 12.2 kg) undertook 3 bouts of resistance-training sessions at 6-repetitions maximum. Countermovement jump (CMJ), lower-body range of motion (ROM), muscle soreness, and creatine kinase (CK) were examined prior to and immediately, 24 h (T24), and 48 h (T48) after each resistance-training bout. Submaximal (i.e., below anaerobic threshold (AT)) and maximal (i.e., above AT) running performances were also conducted at T24 and T48. Most indirect muscle damage markers (i.e., CMJ, ROM, and muscle soreness) and submaximal running performance were significantly improved (P < 0.05; 1.9%) following the third resistance-training bout compared with the second bout. Whilst maximal running performance was also improved following the third bout (P < 0.05; 9.8%) compared with other bouts, the measures were still reduced by 12%-20% versus baseline. However, the increase in CK was attenuated following the second bout (P < 0.05) with no further protection following the third bout (P > 0.05). In conclusion, the initial bout induced the greatest change in CK; however, at least 2 bouts were required to produce protective effects on other indirect muscle damage markers and submaximal running performance measures. This suggests that submaximal running sessions should be avoided for at least 48 h after resistance training until the third bout, although a greater recovery period may be required for maximal running sessions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".