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Record W2974234629 · doi:10.3390/ijerph16183445

Evaluation of Power Production Asymmetry during Cycling in Persons with Multiple Sclerosis

2019· article· en· W2974234629 on OpenAlexaff
John W Farrell, Debra A. Bemben, Christopher D. Black, Daniel J. Larson, Gabriel Pardo, Cecilie Fjeldstad-Pardo, Rebecca Larson

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCyclingAsymmetryAnalysis of varianceIntensity (physics)Physical medicine and rehabilitationProduction (economics)Physical therapyAnimal scienceMedicineInternal medicinePhysicsBiology

Abstract

fetched live from OpenAlex

Lower limb asymmetries have been observed in persons with multiple sclerosis (PwMS), and have been associated with mobility impairment. An incremental cycling test was performed on a cycle ergometer to determine peak power output (PPO) and peak oxygen consumption (VO2peak). Then, participants cycled at 50%, 60%, and 70% of their PPO to assess the contribution of each lower limb to power production. Two-way repeated measures ANOVA was used to detect group × intensity differences in power production asymmetry. Eight PwMS and six healthy individuals (Non-MS) completed the study. No statistically significant (p > 0.05) group × intensity interactions or main effects were present when examining between-limb differences in power production. The current data do not indicate a statistically significant difference in power production asymmetry between groups and exercise intensities. Previous research has established a 10% difference between contralateral limbs as a threshold for asymmetry. The average asymmetry in power production in PwMS exceeded the 10% threshold at all measured outputs, suggesting the presence of asymmetry in power production.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.159
GPT teacher head0.404
Teacher spread0.246 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public Health→Same topicMultiple Sclerosis Research Studies→French-language works237,207→