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Record W2048606708 · doi:10.5402/2012/251750

Exercise Intensity during Treadmill Walking with Gait-Patterned FES among Patients with Incomplete Spinal Cord Injury: Case Series

2012· article· en· W2048606708 on OpenAlexafffund
Masae Miyatani, Kei Masani, Noritaka Kawashima, B. Catharine Craven, T. Adam Thrasher, Miloš R. Popović

Bibliographic record

VenueISRN Rehabilitation · 2012
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of TorontoToronto Rehabilitation Institute
FundersOntario Neurotrauma Foundation
KeywordsFunctional electrical stimulationGaitSpinal cord injuryPhysical medicine and rehabilitationMedicineTreadmillIntensity (physics)Physical therapyGait analysisSpinal cordStimulationInternal medicine

Abstract

fetched live from OpenAlex

Purpose. To determine the feasibility of increasing the cardiopulmonary exercise intensity during walking with gait-patterned functional electrical stimulation (GP-FES) among individuals with motor incomplete SCI. Methods. Two men with motor-incomplete SCI (Subjects A and B, age 45 and 50 years; Level of Injury: C4 and T10; AIS score: D and D, resp.) performed a three sequential four-minute continuous walking sessions [(1) regular gait (non-GP-FES-1); (2) gait with GP-FES (GP-FES); (3) regular gait (non-GP-FES-2)]. Oxygen consumption (Vo2) was measured continuously during trials. Results. Vo2 was higher during GP-FES (Subjects A and B; 14.5 and 19.1 mL/kg/min, resp.) as compared to regular gait (Non-GP-FES-1: Subjects A and B; 13.4 and 17.0: mL/kg/min, resp.; non-GP-FES-2: Subjects A and B; 13.1 and 17.5: mL/kg/min, resp.). Conclusion. The exercise intensity of GP-FES walking was higher than that of regular walking among individuals with motor incomplete SCI. Further investigations are required to determine the clinical relevance of the exercise.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · 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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.214
Teacher spread0.208 · 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 designCase report
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

Citations1
Published2012
Admission routes2
Has abstractyes

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