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Variability of the Response to Immunotherapy Among Sub-groups of Patients With Multiple Sclerosis (4107)

2021· article· en· W4389460455 on OpenAlexaff
Ibrahima Diouf, Charles B. Malpas, Dana Horáková, Eva Havrdová, Francesco Patti, Vahid Shaygannejad, Serkan Özakbaş, Guillermo Izquierdo Ayuso, Sara Eichau Madueño, Magd Zakaria, Marco Onofrj, Alessandra Lugaresi, Raed Alroughani, Alexandre Prat, Marc Girard, Pierre Duquette, Murat Terzi, Cavit Boz, François Grand’Maison, Sherif Hamdy, Patrizia Sola, Diana Ferraro, Pierre Grammond, Recai Türkoğlu, Helmut Butzkueven, Bassem Yamout, Ayşe Altıntaş, Vincent Van Pesch, Davide Maimone, Jeannette Lechner‐Scott, Roberto Bergamaschi, Rana Karabudak, Gerardo Iuliano, Christopher McGuigan, Elisabetta Cartechini, Michael Barnett, Stella Hughes, María José Sá, Ludwig Kappos, Cristina Ramo‐Tello, Edgardo Cristiano, Suzanne Hodgkinson, Daniele Litterio A. Spitaleri, Aysun Soysal, Thor Petersen, Mark Slee, Ernest Butler, Franco Granella, Freek Verheul, Pamela McCombe, Radek Ampapa, Olga Skibina, Julie Prévost, L. G. F. Sinnige, José Luis Sánchez-Menoyo, Steve Vucic, Guy Laureys, Liesbeth Van Hijfte, Dheeraj Khurana, Richard Macdonell, Tamara Castillo‐Triviño, Orla Gray, Eduardo Agüera, Ilya Kister, Cameron Shaw, Norma Deri, Talal Al‐Harbi, Yára Dadalti Fragoso, Tünde Csépány, Ángel Pérez Sempere, Tomáš Kalinčík

Bibliographic record

VenueNeurology · 2021
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsCegep de Saint JeromeCentre intégré de santé et de services sociaux de Chaudière-Appalaches
Fundersnot available
KeywordsArtArt historyCartographyHumanitiesGeography

Abstract

fetched live from OpenAlex

To assess whether patients’ response to disease modifying therapies (DMT) in multiple sclerosis (MS) varies by disease activity (annualised relapse rate, presence of new MRI lesions), disability, age, MS duration or disease phenotype.

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)0.003
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.257
Teacher spread0.231 · 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
Published2021
Admission routes1
Has abstractyes

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