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Record W2004913798 · doi:10.1542/peds.2006-0655

Prognostic Factors for Early Severity in a Childhood Multiple Sclerosis Cohort

2006· article· en· W2004913798 on OpenAlexaff
Yann Mikaeloff, Guillaume Caridade, Saada Assi, Samy Suissa, Marc Tardieu

Bibliographic record

VenuePEDIATRICS · 2006
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill UniversityRoyal Victoria Hospital
Fundersnot available
KeywordsMedicineCohortProportional hazards modelMultiple sclerosisQuartileCohort studyInternal medicinePediatricsMultivariate analysisSeverity of illnessClinically isolated syndromePredictive valueDiseasePsychiatryConfidence interval

Abstract

fetched live from OpenAlex

OBJECTIVE: The goal was to identify prognostic factors for an early severe course in a cohort of patients with childhood-onset multiple sclerosis, for the construction of a predictive tool. METHODS: The cohort consisted of 197 children from the French Kid Sclérose en Plaques neuropediatric cohort with relapsing/remitting multiple sclerosis beginning before the age of 16 years. Patients were included from 1990 to 2003. We used multivariate survival analysis (Cox model) to evaluate the prognostic value of clinical, MRI, and biological covariates at onset for the occurrence of a third attack or severe disability ("severity" outcome). RESULTS: The cohort was monitored for a mean of 5.5 +/- 3.6 years. The "severity" outcome was recorded for 144 patients (73%). The risk of severity was higher for girls, for a time between the first and second attacks of < 1 year, for childhood-onset multiple sclerosis MRI criteria at onset, for an absence of severe mental state changes at onset, and for a progressive course. A derived childhood-onset multiple sclerosis potential index for early severity was found to have a positive predictive value for severity of > 35% for the upper 2 quartiles. CONCLUSIONS: The clinical and MRI prognostic factors for early severity that were identified were used as the basis of a predictive tool, which will be validated in another cohort. This tool should make it possible to identify subgroups at risk of early severe disease and should facilitate therapeutic studies.

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)0.000
metaresearch head score (Gemma)0.004
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 score0.003
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.043
GPT teacher head0.275
Teacher spread0.232 · 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

Citations122
Published2006
Admission routes1
Has abstractyes

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