MétaCan
Menu
Back to cohort
Record W4403920891 · doi:10.3390/diagnostics14212415

Prevention of Overhead Shoulder Injuries in Throwing Athletes: A Systematic Review

2024· review· en· W4403920891 on OpenAlexaboutno aff
Ayrton Moiroux-Sahraoui, Jean Mazeas, N. A. Rodriguez Delgado, Cécile Le Moteux, Mickael Acco, D.M. Maurice, Andreas Bjerregaard, Florian Forelli

Bibliographic record

VenueDiagnostics · 2024
Typereview
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsThrowingAthletesPhysical medicine and rehabilitationOverhead (engineering)Physical therapyMedicineForensic engineeringComputer scienceEngineeringAeronautics

Abstract

fetched live from OpenAlex

(1) Background: Shoulder pathologies are mostly found in overhead sports. Many risk factors have been identified, in particular a deficit in the kinetic chain. The aim of this review was to find out whether prevention by strengthening the kinetic chain can have an impact on the rate of shoulder injury in overhead pitching athletes. (2) Methods: A systematic review of the literature was carried out, including studies on the role of the kinetic chain in the prevention of overhead athletes. The studies used were works published over the last 10 years searched on PubMed, Cochrane Library, PEDro and Science Direct. They were also analyzed by methodological quality scales: the PEDro scale and the Newcastle-Ottawa scale. (3) Results: Eight studies met the inclusion criteria. The studies analyzed revealed a significant correlation between the use of the kinetic chain and the prevention of shoulder injuries, associating factors such as muscle strength, physical performance in tests (CMJ, FMS), static and dynamic balance and the ability to transfer energy from the lower to the upper body. (4) Conclusions: It is important to integrate core stability work and lower limb strengthening to minimize excessive stress on the shoulder complex, while optimizing force production and performance.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
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 head0.077
GPT teacher head0.426
Teacher spread0.349 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations6
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueDiagnosticsSame topicShoulder Injury and TreatmentFrench-language works237,207