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Record W2031077268 · doi:10.1310/sci2004-277

An Exploratory Analysis of the Potential Association Between SCI Secondary Health Conditions and Daily Activities

2014· article· en· W2031077268 on OpenAlexafffund
John Cobb, Frédéric Dumont, Jean Leblond, So Park, Vanessa K. Noonan, Luc Noreau

Bibliographic record

VenueTopics in Spinal Cord Injury Rehabilitation · 2014
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of British ColumbiaUniversité LavalCentre for Interdisciplinary Research in RehabilitationVancouver General HospitalPraxis Spinal Cord Institute
FundersOntario Neurotrauma FoundationRick Hansen Institute
KeywordsMedicineCLARITYAssociation (psychology)Psychological interventionSpinal cord injuryGerontologyNursingPsychiatryPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Secondary health conditions (SHCs) are common following traumatic spinal cord injury (tSCI) and are believed to influence a person's ability to participate in daily activities (DAs). This association should be understood so that health care providers may target interventions with clarity and purpose to manage SHCs and facilitate DAs to maximal effect. OBJECTIVE: To explore the association between SHCs and DAs expressed as the increased chance of not participating as much as wanted in a DA when an SHC is present. METHODS: Community-dwelling persons with tSCI (n = 1,137) responded to the SCI Community Survey. The occurrence and frequency of 21 SHCs were determined. The extent of participation in 26 DAs was measured. The relative risk (RR) of not participating as much as wanted in a DA when a SHC is present was calculated. RESULTS: When some SHC were present, the RR of not participating as much as wanted increased significantly (range, 15%-153%; P < .001). Certain SHCs (light-headedness/dizziness, fatigue, weight problems, constipation, shoulder problems) were associated with a greater chance of not participating in many DAs. No single SHC was associated with every DA and conversely not every DA was associated with an SHC. CONCLUSIONS: Maximizing participation in DAs requires minimizing SHCs in every instance. Understanding the association between SHCs and DAs may facilitate targeted care resulting in less severe SHCs, greater participation in DAs, and benefits to both the individual and society.

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)0.001
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 score0.070
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.393
Teacher spread0.364 · 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

Citations26
Published2014
Admission routes2
Has abstractyes

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