MétaCan
Menu
Back to cohort
Record W3138788803 · doi:10.1177/2325967121990920

Single-Leg Squat Compensations Are Associated With Softball Pitching Pathomechanics in Adolescent Softball Pitchers

2021· article· en· W3138788803 on OpenAlexaff
Kenzie B. Friesen, Regan E. Shaw, David Shannon, Jeffrey R. Dugas, James R. Andrews, Gretchen D. Oliver

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineSquatPhysical medicine and rehabilitationPhysical therapy

Abstract

fetched live from OpenAlex

Background: A lack of lumbopelvic-hip complex (LPHC) stability is often associated with altered pitching mechanics, thus increasing pain and injury susceptibility. The single-leg squat (SLS) is a simple diagnostic tool used to examine LPHC stability. Purpose: To examine the relationship between trunk compensatory kinematics during the SLS and kinematics at foot contact during the windmill pitch. Study Design: Descriptive laboratory study. Methods: Participants included 55 youth and high school softball pitchers (mean age, 12.6 ± 2.2 years; height, 160.0 ± 11.0 cm; weight, 60.8 ± 15.5 kg). Kinematic data were collected at 100 Hz using an electromagnetic tracking device. Participants were asked to complete an SLS on each leg, then throw 3 fastballs at maximal effort. Values of trunk flexion, trunk lateral flexion, and trunk rotation at peak depth of the SLS were used as the dependent variables in 3 separate backward-elimination regression analyses. Independent variables examined at foot contact of the pitch were as follows: trunk flexion, trunk lateral flexion, trunk rotation, center of mass, stride length, and stride knee valgus. Results: The SLS trunk rotation regression ( F(1,56) = 4.980, P = .030) revealed that trunk flexion predicted SLS trunk rotation (SE = 0.068, t = 2.232, P = .030) and explained approximately 7% of the variance in SLS trunk rotation ( R 2 = 0.083, adjusted R 2 = 0.066). The SLS trunk flexion regression ( F(1,56) = 5.755, P = 0.020) revealed that stride knee valgus significantly predicted SLS trunk flexion (SE = 0.256, t = 2.399, P = .020) and explained approximately 8% of variance in SLS trunk flexion ( R 2 = 0.095, adjusted R 2 = 0.078). Conclusion: Additional trunk rotation and trunk flexion at peak depth of the SLS showed increased knee valgus and trunk flexion at foot contact of the pitch, both of which indicate poor LPHC stability during the softball pitch and may increase the potential for injury. Clinical Relevance: Players and coaches should implement SLS analyses to determine their players’ risk for injury and compensation due to poor core stability.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.277
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOrthopaedic Journal of Sports MedicineSame topicShoulder Injury and TreatmentFrench-language works237,207