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Record W2100819847 · doi:10.1136/bmjopen-2014-005718

The Canadian survey of health, lifestyle and ageing with multiple sclerosis: methodology and initial results

2014· article· en· W2100819847 on OpenAlexafffundabout
Michelle Ploughman, Serge Beaulieu, Chelsea Harris, Stephen Hogan, Olivia J Manning, Penelope W Alderdice, John D. Fisk, A. Dessa Sadovnick, Paul O’Connor, Sarah A. Morrow, Luanne M. Metz, Penelope Smyth, Nancy E. Mayo, Ruth Ann Marrie, Katherine Knox, Mark Stefanelli, Marshall Godwin

Bibliographic record

VenueBMJ Open · 2014
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of ManitobaMcGill UniversityUniversity of CalgaryUniversity of SaskatchewanUniversity of AlbertaLondon Health Sciences CentreSt. Michael's HospitalDalhousie UniversityUniversity of British ColumbiaMemorial University of Newfoundland
FundersCanadian Institutes of Health ResearchEMD SeronoPublic Health AgencyHealth CanadaGenentechMultiple Sclerosis SocietyMultiple Sclerosis Society of CanadaUniversity of AlbertaBiogenSaskatoon City Hospital FoundationNewfoundland and Labrador Centre for Applied Health ResearchPhysiotherapy Foundation of CanadaMultiple Sclerosis Scientific Research FoundationArnold P. Gold FoundationTeva Pharmaceutical IndustriesPublic Health Agency of CanadaDaiichi Sankyo EuropeBayer HealthCareSanofi
KeywordsMedicineMultiple sclerosisGerontologyEpidemiologyPublic healthAgeingFamily medicineEnvironmental healthPathologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: People with multiple sclerosis (MS) are living longer so strategies to enhance long-term health are garnering more interest. We aimed to create a profile of ageing with MS in Canada by recruiting 1250 (5% of the Canadian population above 55 years with MS) participants and focusing data collection on health and lifestyle factors, disability, participation and quality of life to determine factors associated with healthy ageing. DESIGN: National multicentre postal survey. SETTING: Recruitment from Canadian MS clinics, MS Society of Canada chapters and newspaper advertisements. PARTICIPANTS: People aged 55 years or older with MS symptoms more than 20 years. OUTCOME MEASURES: Validated outcome measures and custom-designed questions examining MS disease characteristics, living situation, disability, comorbid conditions, fatigue, health behaviours, mental health, social support, impact of MS and others. RESULTS: Of the 921 surveys, 743 were returned (80.7% response rate). Participants (mean age 64.6±6.2 years) reported living with MS symptoms for an average of 32.9±9.5 years and 28.6% were either wheelchair users or bedridden. There was only 5.4% missing data and 709 respondents provided optional qualitative information. According to data derived from the 2012 Canadian Community Health Survey of Canadians above 55 years of age, older people with MS from this survey sample are about eight times less likely to be employed full-time. Older people with MS were less likely to engage in regular physical activity (26.7%) compared with typical older Canadians (45.2%). However, they were more likely to abstain from alcohol and smoking. CONCLUSIONS: Despite barriers to participation, we were able to recruit and gather detailed responses (with good data quality) from a large proportion of older Canadians with MS. The data suggest that this sample of older people with MS is less likely to be employed, are less active and more disabled than other older Canadians.

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)0.006
metaresearch head score (Gemma)0.007
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: none
Teacher disagreement score0.037
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.016
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.454
GPT teacher head0.474
Teacher spread0.019 · 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

Citations43
Published2014
Admission routes3
Has abstractyes

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