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Record W2943282204 · doi:10.1111/sms.13437

Late swing or early stance? A narrative review of hamstring injury mechanisms during high‐speed running

2019· review· en· W2943282204 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2019
Typereview
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsSimon Fraser University
FundersAustralian Government
KeywordsHamstringHamstring injuryPhysical medicine and rehabilitationBicepsMedicineSwingHamstring musclesElectromyographyEccentricPhysical therapyPoison controlInjury preventionEngineering

Abstract

fetched live from OpenAlex

Hamstring injuries are highly prevalent in many running-based sports, and predominantly affect the long head of biceps femoris. Re-injury rates are also high and together lead to considerable time lost from sport. However, the mechanisms for hamstring injury during high-speed running are still not fully understood. Therefore, the aim of this review was to summarize the current literature describing hamstring musculotendon mechanics and electromyography activity during high-speed running, and how they may relate to injury risk. The large eccentric contraction, characterized by peak musculotendon strain and negative work during late swing phase is widely suggested to be potentially injurious. However, it is also argued that high hamstring loads resulting from large joint torques and ground reaction forces during early stance may cause injury. While direct evidence is still lacking, the majority of the literature suggests that the most likely timing of injury is the late swing phase. Future research should aim to prospectively examine the relationship between hamstring musculotendon dynamics and hamstring injury.

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.

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)0.005
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.809
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.374
Teacher spread0.332 · 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