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Record W2921805781 · doi:10.1016/j.berh.2019.02.008

A pragmatic approach to prevent post-traumatic osteoarthritis after sport or exercise-related joint injury

2019· review· en· W2921805781 on OpenAlexafffund
Jackie L. Whittaker, Ewa M. Roos

Bibliographic record

VenueBest Practice & Research Clinical Rheumatology · 2019
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchCanadian HIV Trials Network, Canadian Institutes of Health Research
KeywordsMedicineOsteoarthritisRehabilitationPhysical therapyPhysical medicine and rehabilitationPsychological interventionQuality of life (healthcare)Alternative medicinePathology

Abstract

fetched live from OpenAlex

Lower extremity musculoskeletal injuries are common in sport and exercise, and associated with increased risk of obesity and post-traumatic osteoarthritis (PTOA). Unlike other forms of osteoarthritis, PTOA is common at a younger age and associated with more rapid progression, which may impact career choices, long-term general health and reduce quality of life. Individuals who suffer an activity-related joint injury and present with abnormal joint morphology, elevated adiposity, weak musculature, or become physically inactive are at increased risk of PTOA. Insufficient exercise therapy or incomplete rehabilitation, premature return-to-sport and re-injury, unrealistic expectations, or poor nutrition may further elevate this risk. Delay in surgical interventions in lieu of exercise therapy to optimize muscle strength and neuromuscular control while addressing fear of movement to guarantee resumption of physical activity, completeness of rehabilitation before return-to-sport, education that promotes realistic expectations and self-management, and nutritional counseling are the best approaches for delaying or preventing PTOA.

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)0.012
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.002
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0020.007

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 head0.140
GPT teacher head0.503
Teacher spread0.363 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations93
Published2019
Admission routes2
Has abstractyes

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