Discovery and Clinical Validation of C1M and C4M as Soluble Biomarkers for Diagnosis, Prognosis, and Symptom Prediction in Psoriatic Disease and Other Inflammatory Arthropathies
Bibliographic record
Abstract
Psoriatic disease (PsD) is a complex, heterogeneous disease with unmet medical needs in terms of its diagnosis, management, and prognosis. The identification of biomarkers could improve the implementation of precision medicine in PsD, but to date, none of these biomarkers have been clinically validated. Biomarkers can support clinical trials in several ways, including (1) diagnostics, (2) drug pharmacodynamics, (3) prognostics for patient selection and monitoring of drug efficacy, and (4) predictive models for clinical outcomes. Biomarkers can sometimes be used for both diagnosis and prognosis. Benefits of biomarkers use may include shorter duration of clinical trials, faster access to new treatments, and a personalized approach to disease management. Several potential biomarkers have recently demonstrated promise for use in PsD, including C1M, a serum biomarker reflecting collagen type I collagen degradation, and C4M, a type IV collagen metabolite, but clinical validation has not yet been completed. Here, and as presented at the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis 2024 annual meeting, we summarize the status of biomarker discovery for PsD and their overlap with other musculoskeletal diseases such as rheumatoid arthritis and axial spondyloarthritis.
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 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 | 0.013 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".