Cell population data as predictive biomarkers for biologic therapy response in psoriasis
Bibliographic record
Abstract
Background Biologic agents have significantly improved psoriasis treatment, but patient responses exhibit considerable heterogeneity, highlighting the urgent need for practical predictive biomarkers of therapeutic efficacy.Research design and methods This prospective cohort study enrolled 422 psoriasis patients and 150 healthy controls. Sixteen CPD parameters were measured using a hematology analyzer. We analyzed associations between baseline CPD and disease severity as well as inflammatory markers, assessed their predictive value for treatment response over 48 weeks of biologic therapy, and monitored early dynamic changes in CPD and their relationship with treatment response in 169 patients.Results All CPD parameters were significantly elevated in psoriasis patients compared to healthy controls (all p < 0.001). Baseline mean lymphocyte volume (MN-V-LY) demonstrated sustained negative correlations with PASI improvement rates from weeks 4 to 48 (ρ = −0.278 to −0.449, all p < 0.001). Early reduction in monocyte volume heterogeneity (SD-V-MO) was significantly associated with long-term efficacy (ρ = −0.355 to −0.546, all p < 0.001).Conclusions As simple, standardized hematological parameters, CPD show potential for clinical application in predicting biologic therapy response in psoriasis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".