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Record W3097172345 · doi:10.1016/j.ejro.2020.100285

Validation of assessment methods for the apparent diffusion coefficient in a clinical trial of axial spondyloarthritis patients treated with golimumab

2020· article· en· W3097172345 on OpenAlexaboutno aff
Jakob Møllenbach Møller, Mikkel Østergaard, Henrik S. Thomsen, Simon Krabbe, Inge Juul Sørensen, Bente Jensen, Ole Rintek Madsen, Mette Klarlund, Susanne Juhl Pedersen

Bibliographic record

VenueEuropean Journal of Radiology Open · 2020
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
FundersMerck Sharp and DohmeMSD Life Science Foundation, Public Interest Incorporated Foundation
KeywordsMedicineGolimumabEffective diffusion coefficientDiffusionClinical trialNuclear medicineRadiologyPhysical therapyMedical physicsInternal medicineMagnetic resonance imaging

Abstract

fetched live from OpenAlex

PURPOSE: To compare three region-of-interest (ROI) settings in the assessment of ADC in a clinical trial, and to evaluate the effectiveness of ADC in assessing therapy-induced changes and predicting clinical outcomes. METHODS: In a 52-week clinical trial involving patients with axial spondyloarthritis, mean sacroiliac joint (SIJ) ADC measurements using structured, lesion-based, and index-lesion ROI-settings were assessed at baseline and weeks 4, 16, and 52. Variation among the three ROI-settings, correlations with Spondyloarthritis Research Consortium of Canada (SPARCC)-bone marrow edema (BME) SIJ inflammation indices, standardized response means (SRMs), and effectiveness in predicting clinical outcomes were analyzed. RESULTS: (SRM:0.54-0.67). Baseline ADC and changes in ADC did not predict clinical outcomes. CONCLUSIONS: Lesion-based and index-lesion ROI ADC could both be used to evaluate the effectiveness of tumor necrosis factor inhibitor therapy. None of the methods could predict clinical outcomes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.078
GPT teacher head0.408
Teacher spread0.330 · 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
Published2020
Admission routes1
Has abstractyes

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