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Record W2022632208 · doi:10.1310/sci1802-138

Wheelchair Skill Performance of Manual Wheelchair Users With Spinal Cord Injury

2012· article· en· W2022632208 on OpenAlexaff
M. Oyster, I. Smith, R. Lee Kirby, Rory A. Cooper, Suzanne L. Groah, Jessica Presperin Pedersen, Michael L. Boninger

Bibliographic record

VenueTopics in Spinal Cord Injury Rehabilitation · 2012
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsWheelchairManual wheelchairSpinal cord injuryPhysical medicine and rehabilitationMedicinePhysical therapySpinal cordComputer science

Abstract

fetched live from OpenAlex

Many individuals with a spinal cord injury (SCI) rely on their wheelchairs to complete daily mobility tasks. Unfortunately, the natural environment creates many mobility challenges for wheelchair users. A study by Meyers et al found that wheelchair users reported curbs, uneven terrain, and travel surface as barriers to their mobility.1 To negotiate these mobility tasks, wheelchair users require certain wheelchair skills. A study by Kilkens et al found wheelchair skills performance to be moderately associated with participation.2 Therefore, the inability to perform certain skills can limit a wheelchair user’s functional independence and participation in daily activities. The purpose of this study was to examine wheelchair skill performance of manual wheelchair users with SCI among 6 Model SCI Systems (MSCIS).

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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.035
GPT teacher head0.389
Teacher spread0.355 · 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

Citations16
Published2012
Admission routes1
Has abstractyes

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