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Record W2018193094 · doi:10.1080/09638280310001621451

Neck discomfort of wheelchair users: effect of neck position

2003· article· en· W2018193094 on OpenAlexaff
R. Lee Kirby, Christine L Fahie, Cher Smith, Emma L. Chester, Donald A. MacLeod

Bibliographic record

VenueDisability and Rehabilitation · 2003
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsWheelchairSittingMedicineRepeated measures designNeck musclesPhysical medicine and rehabilitationPhysical therapyOrthodonticsMathematicsComputer scienceAnatomyStatistics

Abstract

fetched live from OpenAlex

PURPOSE: To test the hypothesis that wheelchair users experience more discomfort when holding their necks in extended and/or rotated positions than when in their self-selected most comfortable positions (MCPs). METHODS: We studied 20 wheelchair users, first determining their MCPs with the eyes closed. Then, subjects assumed and maintained (for 5 min each) four neck positions in random order: level (L) and elevated (E), both straight ahead of the subject (S) and with the neck rotated (R). We measured neck extension angles (from digital photographs) and neck discomfort (using visual analogue scales [VAS], in %). RESULTS: The mean neck-extension angles were MCP - 2.6 degrees, LS 9.5 degrees, LR 8.1 degrees, ES 23.9 degrees and ER 25.4 degrees (ANOVA p < 0.0001). The mean VAS neck discomfort scores were LS 5.7%, LR 17.4%, ES 24.0% and ER 34.1% (ANOVA p < 0.0001). CONCLUSIONS: Sustained extension and rotation of the neck, alone or in combination, increase the neck discomfort of wheelchair users. The MCP for most wheelchair users is straight ahead with the neck slightly flexed, about 11 degrees and 27 degrees more flexed, respectively, than when looking at an average-height sitting or standing person. These findings have implications for wheelchair design, the behaviour of clinicians and wheelchair users, and the built environment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.095
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.280
Teacher spread0.274 · 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 teacher head, 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

Citations29
Published2003
Admission routes1
Has abstractyes

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