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Record W3164755758 · doi:10.1177/13524585211016726

Care consumption of people with multiple sclerosis: A multichannel sequence analysis in a population-based setting in British Columbia, Canada

2021· article· en· W3164755758 on OpenAlexafffundabout
Jonathan Le Roux, Elaine Kingwell, Feng Zhu, Helen Tremlett, Emmanuelle Leray

Bibliographic record

VenueMultiple Sclerosis Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineComorbidityConsumption (sociology)CohortPopulationHealth careGerontologyMultidisciplinary approachCohort studyDemographyFamily medicineEnvironmental healthPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Persons with multiple sclerosis (PwMS) typically require complex multidisciplinary care, which is rarely formally assessed. OBJECTIVES: We applied multichannel sequence analysis (MCSA) to identify care consumption patterns by PwMS in British Columbia, Canada. METHODS: We created two cohorts, comprising incident and prevalent MS cases, using linked clinical and administrative data. We applied MCSA to quantify and compare the care pathways of PwMS, based on all-cause hospitalizations and physician visits (divided into five specialities). Care consumption clusters were characterized using demographic and clinical features. RESULTS: From 1048 incident and 3180 prevalent PwMS, the MCSA identified 12 and 6 distinct care consumption clusters over a median follow-up of 9.6 and 13.0 years, respectively. Large disparities between clusters were observed; the median number of annual consultations ranged from 5.6 to 21.3 for general practitioners, 1.2 to 4.6 for neurologists and 0 to 5.3 for psychiatrists in the incident cohort. Characteristics at MS symptom onset associated with the highest care consumption included high comorbidity burden and older age. There were similar disparities and associations for prevalent PwMS. CONCLUSION: The distinct patterns of care consumption, which were reminiscent of the heterogeneity of MS itself, may facilitate health service planning and evaluation, and provide a novel outcome measure in health research.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
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 head0.057
GPT teacher head0.272
Teacher spread0.215 · 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.

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

Citations12
Published2021
Admission routes3
Has abstractyes

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