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Record W4367302609 · doi:10.1212/wnl.0000000000203661

International Consensus on Smoldering Disease in Multiple Sclerosis using the Delphi Method (P11-3.013)

2023· article· en· W4367302609 on OpenAlexaff
Francesca Bagnato, Antonio Scalfari, Jiwon Oh, Laura Airas, Stefan Bittner, Massimiliano Calabrese, José Manuel García‐Domínguez, Cristina Granziera, Benjamin Greenberg, Kerstin Hellwig, Zsolt Illés, Jan Lycke, Anthony Traboulsee, Veronica Popescu, Gavin Giovannoni

Bibliographic record

VenueNeurology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMultiple sclerosisDiseaseMedicineClinical diseaseIntensive care medicineClinical PracticePathologicalNeurodegenerationDisease managementModalitiesNeurosciencePathologyPsychologyImmunologyPhysical therapyParkinson's disease

Abstract

fetched live from OpenAlex

To develop consensus-driven statements on various domains of smoldering MS: definition, onset, underlying pathology, clinical and radiological manifestations, and modalities to detect smoldering disease in clinical practice. A deeper understanding of smoldering disease will optimize clinical management, foster drug discovery through identification of novel targets and help pwMS understand reasons for their decline.

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)0.249
metaresearch head score (Gemma)0.251
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.249
Threshold uncertainty score0.927

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2490.251
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0050.003
Science and technology studies0.0020.003
Scholarly communication0.0020.003
Open science0.0020.011
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.002

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 head0.370
GPT teacher head0.475
Teacher spread0.104 · 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.

Study designQualitative
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

Citations1
Published2023
Admission routes1
Has abstractyes

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