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Record W3211389104 · doi:10.1016/s1474-4422(21)00364-1

Safety and efficacy of teriflunomide in paediatric multiple sclerosis (TERIKIDS): a multicentre, double-blind, phase 3, randomised, placebo-controlled trial

2021· article· en· W3211389104 on OpenAlexaff
Tanuja Chitnis, Brenda Banwell, Ludwig Kappos, Douglas L. Arnold, Kıvılcım Gücüyener, Kumaran Deiva, Н. В. Скрипченко, Liying Cui, Stéphane Saubadu, Wenruo Hu, Myriam Bénamor, Annaig Le-Halpere, Philippe Truffinet, Marc Tardieu, Bénédicte Dubois, Hélène Verhelst, Veneta Bojinova-Tchamova, Jean K. Mah, Fang Fang, Yunpeng Hao, Jiang Li, Ling Li, Ding-An Mao, Wei Qiu, Guojun Tan, Ye Wu, Meini Zhang, Hongyu Zhou, Shuizhen Zhou, Katrin Gross‐Paju, Emmanuel Cheuret, Giles Edan, Sandra Vukusic, George Chrousos, Dimitrios Zafeiriou, Anat Achiron, Adi Vaknin‐Dembinsky, Bassem Yamout, Jūratė Laurynaitienė, Nerija Vaičienė-Magistris, V. Bojkovski, Vesna Trajkova, S. Chaouki, Najib Kissani, Rinze F. Neuteboom, Filipe Palavra, Anna N. Belova, Alexey Boyко, Evgeny Evdoshenko, Е. И. Каирбекова, Nadezhda Malkova, M. V. Shumilina, Natalya Skripchenko, Dimitrije Nikolić, José Meca-Lallana, Chahnez Triki, Mhiri Chokri, Riadh Gouider, Banu Anlar, Ayşe Semra Hız, Egemen İdıman, Recai Türkoğlu, Zühal Yapıcı, Ünsal Yılmaz, Lyudmyla Tantsura, N. N. Voloshyna, Ming Lim, Evangeline Wassmer, Mark Cascione, Christopher LaGanke, Kevin M. Rathke, John Scagnelli

Bibliographic record

VenueThe Lancet Neurology · 2021
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMontreal Neurological Institute and HospitalNeuroRx Research (Canada)McGill University
FundersSanofi
KeywordsTeriflunomideMedicinePlaceboMultiple sclerosisDouble blindPhase (matter)Physical therapyPediatricsInternal medicineAlternative medicinePsychiatryPathologyFingolimodPhysics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
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 head0.085
GPT teacher head0.337
Teacher spread0.253 · 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 designRandomized trial
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

Citations81
Published2021
Admission routes1
Has abstractno

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