Commentaries on Viewpoint: Distinct modalities of eccentric exercise: different recipes, not the same dish
Bibliographic record
Abstract
Exercise volume is a key factor in resistance training. A recent meta-analysis showed comparable exercise volumes led to similar strength gains (5). When performing an isoweight eccentric exercise, the volume can be manipulated a priori, given the total number of repetitions and for each repetition the range of motion, time under tension, and intensity (i.e., the external load) (2). When performing an isokinetic eccentric exercise, the intensity depends on the subject’s ability to gradually or maximally perform each repetition. To possibly match the eccentric isoweight vs isokinetic exercise intensity, we previously calculated the maximal eccentric:concentric isokinetic ratio and then transferred such a ratio as a percentage of the maximal concentric isoweight load (i.e., %1-RM) (2). It resulted in comparable isoweight versus isokinetic training intensity and volume, which led to overall similar muscle strength and architecture changes (2, 3). To further entangle this picture, the eccentric phase of isoinertial exercise is preceded by a maximal explosive-concentric phase (1). Thus, isoinertial cannot be considered as a purely eccentric exercise because of the effects of the eccentric or concentric phase, or the combination of both. Therefore, encouraging new studies to compare isoweight versus isokinetic versus isoinertial eccentric training, we recommend a thorough exercise volume calculation for appropriate matching.
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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 | 0.021 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.036 | 0.039 |
| Insufficient payload (model declined to judge) | 0.028 | 0.015 |
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".