Stress for Vertebral Bodies and Intervertebral Discs with Respect to Squatting Depth
Bibliographic record
Abstract
For the development of speed strength in professional sports, “specific” strength training in the half or the quarter squat have been recommended. Due to the better lever ratios, higher loads have to be used to induce the necessary training stimuli compared to the deep squat. Therefore, intradiscal pressure and compressive forces on vertebral bodies increase. Calculated compressive forces for the L3/L4 vertebral segment were revealed to be 6–10-fold bodyweight when the half or the quarter squat was performed with 0.8–1.6-fold bodyweight. After 10 weeks of training, physical education students have even been able to lift 3.89-fold bodyweight in the one repetition maximum (1-RM) of the quarter squat. The presented dependence of squatting depth, load and their influence on the spinal column have not been discussed before. A search for relevant scientific literature was conducted using PubMed. Concerns about increased risk of injuries in the deep squat have been disproven by plenty of cross-sectional studies with professional athletes. On the contrary, the comparably supramaximal weight loads in the half and the quarter squat should be regarded as increasing injury risks caused by the higher shear and compressive forces in the vertebral column. Therefore, we come to the conclusion that the half and the quarter squat should not further be recommended.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".