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Record W4230357150 · doi:10.1017/s0317167100051234

CJN volume 31 issue 3 Cover and Front matter

2004· article· en· W4230357150 on OpenAlexvenueno aff

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFront coverFront (military)Cover (algebra)Volume (thermodynamics)Action (physics)Content (measure theory)Environmental scienceComputer scienceMathematicsGeographyEngineeringPhysicsMechanical engineeringMeteorologyThermodynamics

Abstract

fetched live from OpenAlex

Rebif relative benefits vs. placebo Reduced relapse rate Delayed progression of disability Reduced burden of disease 0 -7 ) v t .: .f e w i i h p U r t N > .mean, p 0.00M (21.3 vs.I I •* month* «itli [.JJ.IIH.. fir*l qiurlili, p I).»V dcU\ in time tn lonlifmrd lir»l priiyrruiiinI ( » J % n ..10.9V with plat ctx>, median, p-0.0001, a* measured by MR])In two pivotal studies, including a total of 628 patients, Rebif showed significant efficacy in three major outcomes (relapses, disability progression and MRI). 1 ~ Its ability to affect the course of the disease 2 has made Rebif not only a good first-line choice for relapsing-remitting MS, but the leading drug in its class.'Results "l the M meg ll\\ dose si -years.Rebif is generally well-tolerated.The most common adverse events are often manageable and decrease in frequency and severity over time.2t Rebif alters the natural course of relapsing-remitting MS. :Rebif U indicated for the treatment of relapsing-remitting multiple sclerosis in patients with an EDSS between 0 and 5.0, to reduce the number and severity of clinical exacerbations, slow the progression of physical disability, reduce the requirement for steroids, and reduce the number of hospitalizations for treatment of multiple sclerosis.The efficacy of Rebif has been confirmed by T.-Gd enhanced and Ti (burden of disease) MRI evaluations.t The most common adverse events reported are injection-site disorders (all) (92.4% vs. 38.5% placebo), upper respiratory tract infections (74.5% vs. 85.6% placebo), headache (70.1% vs. 62.6% placebo), flu-like symptoms (58.7% vs. 51.3%placebo), fatigue (41.3% vs. 35.8%placebo) and fever (27.7% vs. 15.5% placebo).Evidence of safety and efficacy derived from 2-year data only.Please see product monograph for full prescribing information.t Randomized, double-blind, placebo-controlled trial.Rebif 44 meg TIW group (n=184), Rebif 22 meg TTW group in 189), placebo group (n= 187).'A Fictitious case may not be representative of results for the general population.1%Ag?

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.269
Threshold uncertainty score0.383

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.7310.376

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 head0.034
GPT teacher head0.213
Teacher spread0.179 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2004
Admission routes1
Has abstractyes

Explore more

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