MétaCan
Menu
Back to cohort
Record W1159368331 · doi:10.1097/phm.0000000000000358

Active-Arm Passive-Leg Exercise Improves Cardiovascular Function in Spinal Cord Injury

2015· article· en· W1159368331 on OpenAlexaff
Christopher R. West, Katharine D. Currie, Cameron M. Gee, Andrei V. Krassioukov, Jaimie Borisoff

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2015
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsGF Strong Rehabilitation CentreUniversity of British ColumbiaInternational Collaboration On Repair Discoveries
Fundersnot available
KeywordsMedicineCardiorespiratory fitnessSpinal cord injuryPhysical therapyCardiologyPhysical medicine and rehabilitationSpinal cordInternal medicine

Abstract

fetched live from OpenAlex

In a 43-yr-old male subject with a chronic T3 AIS A spinal cord injury, the acute cardiorespiratory responses to active upper-extremity exercise alone and combined active-arm passive-leg exercise (AAPLE) were investigated, along with the cardiorespiratory, cardiac, vascular, and body composition responses to a 6-wk AAPLE interval training intervention. AAPLE elicited superior acute maximal cardiorespiratory responses compared with upper-extremity exercise alone. In response to a 6-wk interval training regimen, AAPLE caused a 25% increase in peak oxygen uptake, a 10% increase in resting stroke volume, and a 4-fold increase in brachial artery blood flow. Conversely, there were no changes in femoral arterial function, body composition, or bone mineral density in response to training. As a potential clinical intervention, AAPLE may be advantageous over other forms of currently available exercise, owing to the minimal setup time and cost involved and the nonreliance on specialized equipment that is required for other exercise modalities.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 integrity0.0000.000
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 head0.025
GPT teacher head0.368
Teacher spread0.343 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Physical Medicine & RehabilitationSame topicSpinal Cord Injury ResearchFrench-language works237,207