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Record W4389885207 · doi:10.61186/aassjournal.1255

Video Analysis of Acute Hamstring Injury Mechanisms During Deadlifts

2023· article· en· W4389885207 on OpenAlexaff
A. Dao, Flaviu Trifoi, Thomas Qu, Soroush Nedaie, Geoff MacDonald, Amr Elmaraghy

Bibliographic record

VenueAnnals of Applied Sport Science · 2023
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsCNIB FoundationMcMaster UniversityWestern UniversitySt Joseph's Health CentreQueen's University
Fundersnot available
KeywordsHamstringSports biomechanicsKinesiologyHamstring injuryPhysical medicine and rehabilitationSports medicineMedicineInjury preventionPoison controlPsychologyPhysical therapyMedical emergencyEngineeringSimulation

Abstract

fetched live from OpenAlex

Background.Existing studies on the mechanisms leading to acute hamstring injury are limited by reliance on author extrapolation and patient recall for injury details.Objectives.This study aims to determine potential mechanisms for acute hamstring strain injuries during deadlifts via videographic observations in vivo.Methods.Videos were searched on the website "YouTube.com"using each of the phrases "hamstring rupture", "hamstring tear", "hamstring injury", "hamstring strain", and "hamstring pull", combined individually with the terms "deadlift", "powerlifting", and "competition".An orthopaedic surgeon validated 16 video clips based on pre-set criteria.Results.16 injury events were analyzed.Hip flexion (n=11) and knee semi-flexion (n=16) were the most common positions leading to injury.The most common injury pattern was a combination of hip flexion with knee semi-flexion and eccentric hamstring loading (n=11).Concentric hamstring loading was observed leading to injury in 3 cases.Conclusion.Acute hamstring injuries during deadlifts occurred by eccentric hamstring loading with a semi-flexed knee and a flexed or semi-flexed hip, or by concentric hamstring loading with a semi-flexed knee and semi-flexed hip.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
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 head0.026
GPT teacher head0.342
Teacher spread0.316 · 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 designBench or experimental
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

Citations1
Published2023
Admission routes1
Has abstractyes

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