MétaCan
Menu
Back to cohort
Record W2167621932

Scoring inflammatory activity of the spine by magnetic resonance imaging in ankylosing spondylitis: a multireader experiment.

2007· article· en· W2167621932 on OpenAlexaboutno aff
Cédric Lukas, Jürgen Braun, Désirée van der Heijde, Kay‐Geert Hermann, Martín Rudwaleit, Mikkel Østergaard, A. Oostveen, Phil O’Connor, Walter P. Maksymowych, R. Lambert, Anne Grethe Jurik, Xenofon Baraliakos, Robert Landewé

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnkylosing spondylitisMagnetic resonance imagingIntraclass correlationSpondylitisBASDAINuclear medicineRadiologyPhysical therapySurgeryInternal medicineRheumatoid arthritis
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: Magnetic resonance imaging (MRI) of the spine is increasingly important in the assessment of inflammatory activity in clinical trials with patients with ankylosing spondylitis (AS). We investigated feasibility, inter-reader reliability, sensitivity to change, and discriminatory ability of 3 different scoring methods for MRI activity and change in activity of the spine in patients with AS. METHODS: Thirty sets of spinal MRI at baseline and after 24 weeks of followup, derived from a randomized clinical trial comparing a tumor necrosis factor (TNF)-blocking drug (n = 20) with placebo (n = 10) and selected to cover a wide range of activity at baseline and change in activity, were presented electronically in a partial latin-square design to 9 experienced readers from different countries (Europe, Canada). Readers scored each set of MRI 3 times, using 3 different methods including the Ankylosing Spondylitis spine Magnetic Resonance Imaging-activity [ASspiMRI-a, grading activity (0-6) per vertebral unit in 23 units]; the Berlin modification of the ASspiMRI-a; and the Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system, which scores the 6 vertebral units considered by the reader as the most abnormal, with additional scores for "depth" and "intensity." Both the order of the methods used by each reader and the timepoints (before/after treatment) were randomized. Feasibility of each scoring system was evaluated by measuring the mean time needed to score each set of MRI, and inter-reader reliability was evaluated by smallest detectable change (SDC) and by intraclass correlation coefficients (ICC) for all readers together and for all possible reader pairs separately. Sensitivity to change was investigated by calculating Guyatt's effect size on change scores. Discriminatory ability was assessed using Z-scores (Mann-Whitney test) comparing change in score between patients treated with TNF-blocking drug and placebo. RESULTS: The mean time to score one set of MRI was shortest for the Berlin method. SDC was lowest for the Berlin method and highest for SPARCC. Overall inter-reader ICC per method were between 0.49 and 0.77 for scoring activity status, and between 0.46 and 0.72 for scoring activity change. ICC for all possible reader pairs showed much more fluctuation per method, with lowest observed values of about 0.05 (very low agreement) and highest observed values over 0.90 (excellent agreement). In general, ICC for SPARCC were consistently higher than for other systems. Sensitivity to change differed per reader, and was more consistent with SPARCC than with the other methods, but was in general excellent for all 3 methods. Discrimination between groups (TNF-blocker vs placebo) assessed by Z-scores was good and comparable among methods. CONCLUSION: This experiment demonstrates the feasibility of multiple-reader MRI scoring exercises for method comparison, provides evidence for the feasibility, reliability, sensitivity to change, and discriminatory capacity of all 3 tested scoring systems to be used in assessing spinal activity on MRI in patients with AS in clinical trials. On the basis of these results it is not possible to prioritize one of the 3 methods.

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)0.000
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 score0.466
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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

Citations151
Published2007
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicSpondyloarthritis Studies and TreatmentsFrench-language works237,207