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Record W2806510983 · doi:10.2217/ebo.12.515

Imaging features in psoriatic arthritis

2013· other· en· W2806510983 on OpenAlexaff
Wilson Bautista‐Molano, Désirée van der Heijde

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsArthritis Society
Fundersnot available
KeywordsPsoriatic arthritisDermatologyMedicinePsoriasisComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Psoriatic arthritis (PsA) is characterized by a diverse array of musculoskeletal pathology involving the joints and peri-articular structures in both the peripheral and axial skeleton. For a long time, PsA was not recognized as a specific entity but was rather considered a subtype of rheumatoid arthritis that occurred in combination with skin psoriasis and even was considered to be a mild disease with a benign course. Therefore, research in PsA has long been behind rheumatoid arthritis in terms of not only diagnosis, prognosis and treatment but also imaging. The clinical spectrum of PsA is very diverse, involving the spine, sacroiliac joints, peripheral joints and/or entheses. PsA is characterized by entheseal pathology, usually in the context of a present or past history of psoriasis. Other manifestations are small joint polysynovitis and diffuse osteitis closely linked to enthesitis. Damage of these structures and disease activity can be imaged using a variety of modalities, including conventional radiography, ultrasonography and MRI.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.006
GPT teacher head0.247
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2013
Admission routes1
Has abstractyes

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