Neuromuscular Physiology, Exercise, and Training During Youth—The Year That Was 2017
Bibliographic record
Abstract
The pressure for children to excel and succeed in sport continues to mount. Although resistance training for youth was in disfavor by many organizations even into the early 21st century, children's training programs are more closely resembling the volume and intensity of adult programs. The physiological maturation of adolescent youth may impact their response to advanced training programs. Furthermore, the pressure to specialize in specific sports rather than engage in a variety of sporting activities may affect not only training responses but also injury incidence. The highlighted articles first illustrate the training-specific responses of prepeak and postpeak height velocity stage youth with more specific training stimuli needed for the postpeak height velocity stage youth. Second, individual sports tend to promote earlier and greater specialization compared with team sports, which tend to result in a higher proportion of overuse injuries. Based on the findings of these 2 studies, the planning and implementation of high-intensity training for youth, such as plyometrics, should take into consideration the physical maturation of the child and that the prevention of overuse injuries would benefit from a more varied participation in sports and activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".