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Machine Learning-Based Classification of Acute versus Chronic Multiple Sclerosis Lesions using Radiomic Features from Unenhanced Cross-Sectional Brain MRI (4121)

2021· article· en· W3210087576 on OpenAlexaff
Bastien Caba, Dawei Liu, Aurélien Lombard, Natasha Novikov, Alexandre Cafaro, Daniel P. Bradley, Enzo Battistella, Elizabeth Fisher, Nathalie Franchimont, Arie Gafson, Parya MomayyezSiahkal, Zahra Karimaghaloo, Douglas L. Arnold, Colm Elliott, Nikos Paragios, Shibeshih Belachew

Bibliographic record

VenueNeurology · 2021
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsMcGill UniversityNeuroRx Research (Canada)
Fundersnot available
KeywordsMedicineMultiple sclerosisMagnetic resonance imagingRadiologyPsychiatry

Abstract

fetched live from OpenAlex

To build a machine learning/artificial intelligence-based (ML/AI) tool to classify acute (T1 gadolinium-enhancing [T1Gd+] or new T2 hyperintense lesions) versus chronic T2 hyperintense multiple sclerosis (MS) lesions using only cross-sectional T1- and T2-weighted brain MRI without gadolinium contrast information.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.721
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.041
GPT teacher head0.324
Teacher spread0.283 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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