MétaCan
Menu
Back to cohort
Record W2767375489 · doi:10.1177/1352458517739990

The NAIMS cooperative pilot project: Design, implementation and future directions

2017· article· en· W2767375489 on OpenAlexaff
Jiwon Oh, Rohit Bakshi, Peter A. Calabresi, Ciprian M. Crainiceanu, Roland G. Henry, Govind Nair, Nico Papinutto, R. Todd Constable, Daniel S. Reich, Daniel Pelletier, William D. Rooney, Daniel L. Schwartz, Ian Tagge, Russell T. Shinohara, Jack H. Simon, Nancy L. Sicotte

Bibliographic record

VenueMultiple Sclerosis Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Toronto
FundersNational Center for Advancing Translational SciencesNational Institute of Neurological Disorders and StrokeNational Institutes of Health
KeywordsStandardizationProtocol (science)Magnetic resonance imagingMedical physicsComputer scienceReproducibilityPaceMultiple sclerosisMedicineRadiologyPathologyPhysicsOperating system

Abstract

fetched live from OpenAlex

The North American Imaging in Multiple Sclerosis (NAIMS) Cooperative represents a network of 27 academic centers focused on accelerating the pace of magnetic resonance imaging (MRI) research in multiple sclerosis (MS) through idea exchange and collaboration. Recently, NAIMS completed its first project evaluating the feasibility of implementation and reproducibility of quantitative MRI measures derived from scanning a single MS patient using a high-resolution 3T protocol at seven sites. The results showed the feasibility of utilizing advanced quantitative MRI measures in multicenter studies and demonstrated the importance of careful standardization of scanning protocols, central image processing, and strategies to account for inter-site variability.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0010.000
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 head0.197
GPT teacher head0.387
Teacher spread0.191 · 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.

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

Citations18
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueMultiple Sclerosis JournalSame topicMultiple Sclerosis Research StudiesFrench-language works237,207