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Record W2039898794 · doi:10.3109/17483100903391145

Are wheelchair-skills assessment and training relevant for long-standing wheelchair users? Two case reports

2010· article· en· W2039898794 on OpenAlexaff
Anita Mountain, Cher Smith, R. Lee Kirby

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2010
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsWheelchairPhysical medicine and rehabilitationTraining (meteorology)Applied psychologyPsychologyPhysical therapyComputer scienceHuman–computer interactionMedicineWorld Wide WebGeography

Abstract

fetched live from OpenAlex

PURPOSE: We present two case reports that shed light on the question of whether routine periodic wheelchair-skills assessment and training are relevant for long-standing wheelchair users. CASE 1: A 60-year-old man with a 15-year history of T12 complete paraplegia sustained an intertrochanteric fracture of his femur due to a tip-over accident that occurred 2 days after a follow-up clinic visit at which no limitations in wheelchair-skill performance were identified. If a procedure had been in place to identify and correct his wheelchair-skill deficiencies, this injury might have been prevented. CASE 2: A 34-year-old woman with spina bifida, whose wheelchair use had gradually increased, came to our attention during the provision of a new wheelchair. She was able to significantly improve her wheelchair abilities through training. The newly learned skills enhanced her community participation. CONCLUSIONS: These cases suggest that, even in long-standing wheelchair users, wheelchair skills should be routinely assessed as part of the periodic functional assessment and, when the skill level is determined to be less than appropriate for that person, formal training should be offered.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.028
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.406
Teacher spread0.373 · 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

Citations13
Published2010
Admission routes1
Has abstractyes

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