MétaCan
Menu
Back to cohort
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 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.370
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 teacher head, 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueNeurologySame topicDelphi Technique in ResearchFrench-language works237,207