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Record W4248894536 · doi:10.31231/osf.io/yvbpg

Yoga in Rheumatic Diseases

2019· preprint· en· W4248894536 on OpenAlexaff
Susan J. Bartlett, Steffany Moonaz, Christopher P. Mill, Sasha Bernatsky, Clifton O. Bingham

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsMcGill UniversityRoyal Victoria HospitalMcGill University Health Centre
Fundersnot available
KeywordsMindfulnessMeditationFlexibility (engineering)Physical therapyMedicineQuality of life (healthcare)AnxietyAlternative medicineRelaxation (psychology)Mindfulness meditationBreathing exercisesCalmnessProprioceptionPhysical medicine and rehabilitationFibromyalgiaBreathingPsychologyPsychotherapistPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Yoga is a popular activity which may be well suited for some individuals with certain rheumatic disorders. Regular yoga practice can increase muscle strength and endurance, proprioception and balance, with emphasis on movement through a full range of motion to increase flexibility and mobility. Additional beneficial elements of yoga include breathing, relaxation, body awareness and meditation, which can reduce stress and anxiety and promote a sense of calmness, general well-being and improved quality of life. Yoga also encourages a meditative focus, increased body awareness and mindfulness; some evidence suggests yoga may help decrease inflammatory mediators including C-reactive protein and interleukin-6. Yoga is best learned under the supervision of qualified teachers who are well informed about the potential musculoskeletal needs of each individual. Here, we briefly review the literature on yoga in healthy, musculoskeletal, and rheumatic disease populations and offer recommendations for discussing ways to begin yoga with patients.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.030
GPT teacher head0.324
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2019
Admission routes1
Has abstractyes

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