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A Population-Based Study Comparing Multiple Sclerosis Clinic Users and Non-Users in British Columbia, Canada (P3.354)

2016· article· en· W2550996794 on OpenAlexaffabout
Kyla A. McKay, Helen Tremlett, Feng Zhu, Lorne F. Kastrukoff, Ruth Ann Marrie, Elaine Kingwell

Bibliographic record

VenueNeurology · 2016
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsUniversity of ManitobaUniversity of British Columbia
Fundersnot available
KeywordsMultiple sclerosisPopulationMedicineGerontologyFamily medicineDemographyPsychiatryEnvironmental healthSociology

Abstract

fetched live from OpenAlex

Background: Much clinical knowledge about multiple sclerosis (MS) has been gained from patients who attend MS specialty clinics. However, there is limited information about whether these patients are representative of the wider MS population, or how they differ from non-clinic users. Objective: To compare incident MS cases who were MS clinic users (from the British Columbia MS database) to those who were non-users of the specialty MS clinics in BC, Canada Design/Methods: This was a retrospective record linkage cohort study using prospectively collected data from the BCMS database and province-wide health administrative databases. Incident MS cases were identified in the general population using a validated algorithm of hospital and physician diagnostic codes. Results: There were 2,928 incident MS cases in BC between 1996 and 2004 including 1,735 (59[percnt]) who had registered at a BC MS clinic (‘clinic cases’) and 1,193 (41[percnt]) who had not registered at a BC MS clinic (‘non-clinic cases’) during the same period. The distributions of sex and socioeconomic status were similar between the groups. However, the non-clinic cases were older, accessed health services more frequently, and had a higher burden of comorbidity than the clinic cases. Only 1[percnt] of the non-clinic cases had filled a prescription for an MS-specific disease-modifying therapy, compared to 49[percnt] of the clinic cases. Conclusions: Our findings have several important implications: even within a publicly funded healthcare system, a high proportion of individuals with MS may not access a specialty MS clinic; the needs of MS patients managed in the community may differ from those referred to an MS clinic; findings from studies involving clinic-based MS cohorts may not always be generalizable to the wider MS population; and access to population-based health administrative data offers the opportunity to gain a broader understanding of MS. Study supported by: CIHR, MS Society of Canada

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.296
Teacher spread0.244 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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