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Record W2158735739 · doi:10.1080/13638490500126707

Wheeling efficiency: The effects of varying tyre pressure with children and adolescents

2006· article· en· W2158735739 on OpenAlexaff
Bonita Sawatzky, Ian Denison

Bibliographic record

VenuePediatric Rehabilitation · 2006
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsGF Strong Rehabilitation CentreBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsWheelingWheelchairEnergy expenditureInflation (cosmology)Heart rateRandomized controlled trialBlood pressurePhysical therapyMedicinePhysical medicine and rehabilitationEngineeringSurgeryComputer sciencePhysicsInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Clinicians often observe child wheelchair users wheeling on tyres that are not inflated to manufacturer's recommendations. The purpose of this study was to investigate changes in energy expenditure that are related to decreased tyre pressure. METHODS: A within subject repeated measures design was used to assess the energy requirements of wheeling with four randomized tire inflation levels (25, 50, 75 and 100% of recommended tire pressure, 100 psi). All 10 subjects (mean age 14.2 +/- 2.3 years completed four 5-minute trials (one for each tyre pressure), while wheeling at a constant, self-selected velocity. Heart rate and wheeling velocity were measured. RESULTS: There was no change in wheeling velocity with changes in tyre pressure; however, energy expenditure was found to increase by over 15% with decreasing tyre pressure (p < 0.05). CONCLUSIONS: In order for children to minimize their energy expenditure and, thus, improve their independence, clinicians and parents must be educated as to the importance of regular wheelchair tyre inflation regimes.

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)0.003
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.004
GPT teacher head0.264
Teacher spread0.260 · 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

Citations12
Published2006
Admission routes1
Has abstractyes

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