Assessing differential responders and mean changes in muscle size, strength, and the crossover effect to 2 distinct resistance training protocols
Bibliographic record
Abstract
The objective of this study was to determine differences in 2 distinct resistance training protocols and if true variability can be detected after accounting for random error. Individuals (n = 151) were randomly assigned to 1 of 3 groups: (i) a traditional exercise group performing 4 sets to failure; (ii) a group performing a 1-repetition maximum (1RM) test; and (iii) a time-matched nonexercise control group. Both exercise groups performed 18 sessions of elbow flexion exercise over 6 weeks. While both training groups increased 1RM strength similarly (∼2.4 kg), true variability was only present in the traditional exercise group (true variability = 1.80 kg). Only the 1RM group increased untrained arm 1RM strength (1.5 kg), while only the traditional group increased ultrasound measured muscle thickness (∼0.23 cm). Despite these mean increases, no true variability was present for untrained arm strength or muscle hypertrophy in either training group. In conclusion, these findings demonstrate the importance of taking into consideration the magnitude of random error when classifying differential responders, as many studies may be classifying high and low responders as those who have the greatest amount of random error present. Additionally, our mean results demonstrate that strength is largely driven by task specificity, and the crossover effect of strength may be load dependent. Novelty Many studies examining differential responders to exercise do not account for random error. True variability was present in 1RM strength gains, but the variability in muscle hypertrophy and isokinetic strength changes could not be distinguished from random error. The crossover effect of strength may differ based on the protocol employed.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".