MétaCan
Menu
Back to cohort
Record W2139788898 · doi:10.3899/jrheum.120244

The Radiological Assessment of Axial Involvement in Psoriatic Arthritis

2012· article· en· W2139788898 on OpenAlexvenueno aff
Ennio Lubrano, Antonio Marchesoni, Ignazio Olivieri, Salvatore D’Angelo, Carlo Palazzi, Raffaele Scarpa, Nicola Ferrara, Wendy J. Parsons, Luca Brunese, Philip Helliwell, Antonio Spadaro

Bibliographic record

VenueJournal of Rheumatology Supplement · 2012
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsRadiological weaponMedicineAnkylosing spondylitisPsoriatic arthritisSacroiliitisRadiologyNuclear medicineInternal medicineDisease

Abstract

fetched live from OpenAlex

This article summarizes the state of radiological assessment of axial involvement in psoriatic arthritis (PsA). The definition and measurement of axial disease in PsA remain problematic and this situation in turn could affect the choice of approach to evaluate radiological findings of the spine. At present, the radiological assessment has been evaluated by using scoring systems borrowed from ankylosing spondylitis (AS). In particular, the Bath AS Radiology Index (BASRI) and the modified Stoke AS Spine Score (m-SASSS) have been validated for axial PsA. A recent study showed that BASRI and m-SASSS were valid instruments; however, neither score encompassed all radiological features of PsA. Therefore, a new index for assessing radiological axial involvement in PsA was developed--the PsA Spondylitis Radiology Index (PASRI). This new index encompassed a greater range of the spinal radiological features of PsA, providing a greater score range, and it correlated well with anthropometric and patient-reported outcomes. Recently, a study assessed the sensitivity to change of BASRI, m-SASSS, and PASRI, and showed that these 3 instruments provided a moderate sensitivity to change but high specificity to detect the true changes.

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)0.000
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 score0.053
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.315
Teacher spread0.295 · 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

Citations18
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Rheumatology SupplementSame topicSpondyloarthritis Studies and TreatmentsFrench-language works237,207