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Record W4410155113 · doi:10.1093/braincomms/fcaf180

Higher healthcare use before paediatric multiple sclerosis onset: a nationwide cohort study

2025· article· en· W4410155113 on OpenAlexaff
Kyla A. McKay, Ali Manouchehrinia, Feng Zhu, Yinshan Zhao, Ruth Ann Marrie, Colleen J. Maxwell, Jan Hillert, Helen Tremlett

Bibliographic record

VenueBrain Communications · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of WaterlooDalhousie UniversityUniversity of British ColumbiaAlberta Health Services
FundersNational Multiple Sclerosis Society
KeywordsCohortMedicineMultiple sclerosisHealth careCohort studyPediatricsPolitical scienceInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Evidence of increased healthcare use occurring before paediatric-onset multiple sclerosis presentation suggests a prodromal phase. However, little is known of its duration or features, and few studies have accessed a clinical cohort to examine the period before symptom onset. We compared annual rates of healthcare use before paediatric multiple sclerosis onset in clinical and administrative cohorts versus matched non-multiple sclerosis cohorts. We identified persons with paediatric-onset multiple sclerosis from the Swedish Multiple Sclerosis registry and population-based administrative data using a validated algorithm requiring ≥3 hospital or outpatient multiple sclerosis diagnostic codes recorded on separate dates. The index date was multiple sclerosis symptom onset, as recorded in the multiple sclerosis registry by a neurologist (clinical cohort) or the earliest demyelinating disease-related International Classification of Diseases code (administrative cohort). Individuals with age at index <18 years were matched with up to five individuals from the general population on sex, birth year, county of residence at the index date and residency time. Healthcare use was measured as hospital/outpatient diagnoses (International Classification of Diseases chapters) and prescription drug classes (Anatomical Therapeutic Chemical classification system, 2nd level). Yearly rates of hospital and outpatient visits (up to 17 years pre-index) and prescription fills (up to 14 years pre-index) were compared using Quasi-Poisson regression. The clinical/administrative cohorts included 233/206 paediatric-onset multiple sclerosis and 1151/1011 matched individuals, with a mean age at the index of 16 years (standard deviation: 2) in all four groups. In both cohorts, individuals with paediatric-onset multiple sclerosis exhibited elevated healthcare use predominantly 1-10 years pre-index, including, for example, higher prescriptions filled for corticosteroids for dermatological use (rate ratio range: 2.61-3.91) and outpatient visits for ill-defined signs/symptoms (rate ratio range: 2.17-8.64) and unassigned ICD codes (rate ratio range: 2.20-4.17). In the year pre-index, individuals with paediatric-onset multiple sclerosis in both cohorts exhibited higher rates of outpatient visits for neoplasms, nervous system disorders, sense organ conditions, ill-defined signs/symptoms and 'other health system contact' (rate ratio range: 2.05-18.00). In the same year, the clinical cohort also had higher rates of prescription fills for 'other gynecologicals' (4.08, 95% confidence interval: 1.04-16.09), and the administrative cohort had higher rates for prescriptions filled across eight drug classes (rate ratio range: 1.56-6.49). Healthcare use was higher primarily in the 1-10 years before paediatric-onset multiple sclerosis versus a matched cohort, suggestive of a prodromal phase. During this period, the paediatric-onset multiple sclerosis cohort was more often identified as having ill-defined signs/symptoms, neoplasms and skin-related issues.

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)0.005
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 score0.265
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.145
GPT teacher head0.385
Teacher spread0.240 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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