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Record W2597277418 · doi:10.14288/hfjc.v9i3.222

Alternating Passive Leg Cycling and Functional Electrical Stimulation: A Novel Approach in Clinical Exercise Rehabilitation for Spinal Cord Injury

2017· article· en· W2597277418 on OpenAlexaff
Henry Lai, Darren E. R. Warburton

Bibliographic record

VenueOpen Collections · 2017
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpinal cord injuryCardiorespiratory fitnessFunctional electrical stimulationMedicineRehabilitationPhysical medicine and rehabilitationStimulationPhysical therapySpinal cordInternal medicine

Abstract

fetched live from OpenAlex

Passive leg cycling and functional electrical stimulation are interventions used to improve the cardiovascular function in persons with spinal cord injury. Although the volume of evidence in support of these techniques is relatively limited, current findings have demonstrated their capacity to improve arterial function in the patients living with spinal cord injury. We proposed that a novel strategy is to alternate the application of passive leg cycling and functional electrical stimulation, which may elicit a synergistic cardiorespiratory response to exercise, leading to marked enhancements in cardiovascular function. This approach has the potential to improve the quality and efficacy of clinical exercise rehabilitation for persons with spinal cord injury, improve their cardiovascular health, and reduce their risks associated with cardiovascular disease.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.163
GPT teacher head0.486
Teacher spread0.323 · 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 designBench or experimental
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

Citations0
Published2017
Admission routes1
Has abstractyes

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