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Record W2137938479 · doi:10.7547/0003-0538-104.2.211

A Theoretical Perspective on Running-Related Injuries

2014· review· en· W2137938479 on OpenAlexaff
Jodi L. Gallant, M.R. Pierrynowski

Bibliographic record

VenueJournal of the American Podiatric Medical Association · 2014
Typereview
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBarefootMedicinePerspective (graphical)Physical medicine and rehabilitationSubtalar jointInjury preventionPoison controlPhysical therapyCognitive psychologyMedical emergencyPsychologyComputer scienceSurgeryAnkleArtificial intelligence

Abstract

fetched live from OpenAlex

The etiology of running-related injuries remains unknown; however, an implicit theory underlies much of the conventional research and practice in the prevention of these injuries. This theory posits that the cause of running-related injuries lies in the high-impact forces experienced when the foot contacts the ground and the subsequent abnormal movement of the subtalar joint. The application of this theory is seen in the design of the modern running shoe, with cushioning, support, and motion control. However, a new theory is emerging that suggests that it is the use of these modern running shoes that has caused a maladaptive running style, which contributes to a high incidence of injury among runners. The suggested application of this theory is to cease use of the modern running shoe and transition to barefoot or minimalist running. This new running paradigm, which is at present inadequately defined, is proposed to avoid the adverse biomechanical effects of the modern running shoe. Future research should rigorously define and then test both theories regarding their ability to discover the etiology of running-related injury. Once discovered, the putative cause of running-related injury will then provide an evidence-based rationale for clinical prevention and treatment.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.906
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.007
GPT teacher head0.278
Teacher spread0.271 · 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 designNot applicable
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

Citations19
Published2014
Admission routes1
Has abstractyes

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