Does core exercises important to functinal training protocols?
Bibliographic record
Abstract
Objective: Our aim is to analyze the effects of 12 weeks of functional training with and without core exercises on core functional and performance indicators. Method: This is a three-arm randomized controlled trial, which will take place over 12 weeks. Participants will be randomly grouped into three training programs, namely: functional training group, which will perform global, multi-articular, and functional exercises, with no exercises for the core; functional training + core group, which will perform a similar protocol to the functional training group, but with the inclusion of specific exercises for the core region; and core training group, which will only perform specific exercises for the core. In both moments, tests will be carried out in the following order: McGill's torso muscular endurance test battery, unilateral hip bridge endurance test, sit up test, isometric dead lift, push up, sit to stand, functional movement screen, handgrip test, countermovement maximal vertical jump test, one repetition maximum in bench press, row and leg press, T- run agility test, Yo-Yo test. Discussion: These findings will provide new evidence to aid physical education professionals in decision-making regarding exercise prescription. Conclusion: We hypothesize that the inclusion of exercises specifically targeting the trunk in functional training protocols will lead to higher functional and core performance
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".