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Record W2430139010 · doi:10.3390/jfmk1020254

Stress for Vertebral Bodies and Intervertebral Discs with Respect to Squatting Depth

2016· article· en· W2430139010 on OpenAlexaboutno aff
Hagen Hartmann, Klaus Wirth, Christoph Mickel, Michael Keiner, André Sander, Dennis Yaghobi

Bibliographic record

VenueJournal of Functional Morphology and Kinesiology · 2016
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsnot available
Fundersnot available
KeywordsSquatSquatting positionLeverLift (data mining)MedicineVertebral columnQuarter (Canadian coin)Physical medicine and rehabilitationOrthodonticsPhysical therapyAnatomyComputer scienceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.283
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2016
Admission routes1
Has abstractyes

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