Combining Clinical, Genetic and Protein Markers Using Machine Learning Models Discriminates Psoriatic Arthritis Patients From Those With Psoriasis
Bibliographic record
Abstract
Background Psoriatic Arthritis (PsA), an immune mediated inflammatory arthritis, affects a quarter of patients with cutaneous psoriasis, usually after psoriasis onset. Early diagnosis of PsA is challenging. A biomarker-based diagnostic test may facilitate early diagnosis. Objectives We aimed to determine whether specific clinical features or genetic and protein markers, alone or in combination, can distinguish patients with PsA from those with psoriasis without PsA (PsC). Methods Patients with PsA and PsC were identified from a database of patients with psoriatic disease. Detailed demographic and clinical information were collected at time of assessment. Single-nucleotide polymorphisms (SNPs) of 19 “PsA weighted” genes were genotyped. Serum samples were used to assess 15 protein markers by ELISA. Association between clinical, genetic and protein markers and PsA were determined, and models were developed to discriminate PsA from PsC using machine learning algorithms. Results Demographic and clinical information had low predictive value in distinguishing PsA from PsC (AUC - 0.607, P < .01). SNP and protein panels also had low value in discriminating PsA from PsC (AUC - 0.691, P < .001 and AUC - 0.694, P < .001, respectively). Combining protein, SNPs and clinical features provided better discriminatory value (best performing model: Random Forest, AUC - 0.733, P < .001). Conclusion Combining previously identified clinical, genetic and protein markers have a fair ability to differentiate PsA from PsC. Further studies are required for identifying better diagnostic signatures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".