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Locus for severity implicates CNS resilience in progression of multiple sclerosis
Adil Harroud, Pernilla Stridh, Jacob L. McCauley, Janna Saarela, Aletta M.R. van den Bosch, Hendrik J. Engelenburg, Ashley Beecham, Lars Alfredsson, Katayoun Alikhani, Lilyana Amezcua, Till F. M. Andlauer, Maria Ban, Lisa F. Barcellos, Nadia Barizzone, Tone Berge, Achim Berthele, Stefan Bittner, Steffan D. Bos, Farren Briggs, Stacy J. Caillier, Peter A. Calabresi, Domenico Caputo, David X. Carmona-Burgos, Paola Cavalla, Elisabeth Gulowsen Celius, Gabriel Cerono, Ángel Chinea, Tanuja Chitnis, Ferdinando Clarelli, Manuel Comabella, Gıancarlo Comı, Chris Cotsapas, Bruce Cree, Sandra D’Alfonso, Efthimios Dardiotis, Philip L. De Jager, Silvia Delgado, Bénédicte Dubois, Sinah Engel, Federica Esposito, Marzena J. Fabis‐Pedrini, Massimo Filippi, Kathryn C. Fitzgerald, Christiane Gasperi, Lissette Gomez, Refujia Gomez, Georgios M. Hadjigeorgiou, Jörg Hamann, Friederike Held, Roland G. Henry, Jan Hillert, Jesse Huang, Inge Huitinga, Talat Islam, Noriko Isobe, Maja Jagodic, Allan G. Kermode, Michael Khalil, Trevor J. Kilpatrick, Ioanna Konidari, Karim L. Kreft, Jeannette Lechner‐Scott, Maurizio Leone, Felix Luessi, Sunny Malhotra, Ali Manouchehrinia, Clara P. Manrique, Filippo Martinelli Boneschi, Andrea C. Martinez, Viviana Martínez-Maldonado, Elisabetta Mascia, Luanne M. Metz, Luciana Midaglia, Xavier Montalbán, Jorge R. Oksenberg, Tomas Olsson, Annette Oturai, Kimmo Pääkkönen, Grant P. Parnell, Nikolaos A. Patsopoulos, Margaret A. Pericak‐Vance, Fredrik Piehl, Justin P. Rubio, Adam Santaniello, Silvia Santoro, Catherine Schaefer, Finn Sellebjerg, Hengameh Shams, Klementy Shchetynsky, Cláudia Silva, Vasileios Siokas, Helle Bach Søndergaard, Melissa Sorosina, Bruce Taylor, Marijne Vandebergh, Domizia Vecchio, Margarete M. Voortman, Howard L. Weiner, Dennis Wever, V. Wee Yong, David A. Hafler, Graeme J. Stewart, Alastair Compston, Frauke Zipp, Hanne F. Harbo, Bernhard Hemmer, An Goris, Joost Smolders, Stephen L. Hauser, Ingrid Kockum, Stephen Sawcer, Sergio E. Baranzini, Ingileif Jónsdóttir, Yolanda Blanco, Sara Llufriú, Lohith Madireddy, Albert Saiz, Pablo Villoslada, Kāri Stefánsson
Bibliographic record
VenueNature · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNational Institute of Environmental Health SciencesNational Institute of Neurological Disorders and StrokeNational Institute of Allergy and Infectious DiseasesNational Institute of Nursing ResearchNIHR Cambridge Biomedical Research CentreMultiple Sclerosis Society of Western AustraliaEMD SeronoKaiser PermanenteNational Multiple Sclerosis SocietyRed Española de Esclerosis MúltipleMitsubishi Tanabe Pharma CorporationSanofiTG TherapeuticsForskningsrådet om Hälsa, Arbetsliv och VälfärdVetenskapsrådetMenzies Institute for Medical ResearchKnut och Alice Wallenbergs StiftelseNovartis Institutes for BioMedical ResearchSanofi GenzymeKU LeuvenFonds Wetenschappelijk OnderzoekSun Yat-sen UniversityBundesministerium für Bildung und ForschungBristol-Myers Squibb FoundationDeutsche ForschungsgemeinschaftMultiple Sclerosis SocietyErasmus Universitair Medisch Centrum RotterdamEuropean CommissionGenentechNational Institute for Health and Care ResearchVlaamse regeringInternational Progressive MS AllianceNational Institutes of HealthRegeneron PharmaceuticalsMax-Planck-GesellschaftTeva Pharmaceutical IndustriesHelsingin YliopistoBristol-Myers SquibbUniversity of TasmaniaYale UniversityAlexion PharmaceuticalsBiogenUniversità degli Studi del Piemonte OrientaleCelgeneIdorsia PharmaceuticalsInstituto de Salud Carlos IIIDisarm TherapeuticsEli Lilly and CompanyRace to Erase MSMultiple Sclerosis Society of CanadaAmgenUniversity of California, San FranciscoMinistero della Salute
KeywordsMultiple sclerosisLocus (genetics)NeurosciencePsychologyResilience (materials science)BiologyGeneticsGenePsychiatry
Abstract
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 machine prediction
Teacher imitationNot 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)0.002
metaresearch head score (Gemma)0.010
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 score0.005
Threshold uncertainty score0.017
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 head0.061
GPT teacher head0.378
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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
Citations227
Published2023
Admission routes2
Has abstractno