A novel nomogram based on clinical features and laboratory parameters to predict biologic-refractory psoriasis patients
Bibliographic record
Abstract
PURPOSE: Biologic-refractory psoriasis has emerged as an area of unmet need in a landscape of generally well-controlled disease. The study aimed to establish a predictive model grounded in the clinical features and laboratory parameters to assess the risk of biologic-refractory patient (BRP) prior to initiating biologic therapy. MATERIALS AND METHODS: Biologic-naïve psoriasis patients who initiated their first biologic at the Department of Dermatology of Xiangya Hospital were included and randomized into training and validation sets in a 6:4 ratio. Logistic regression and lasso analysis were performed to screen the risk variables for BRP status. RESULTS: Seven hundred and forty-two psoriatic patients comprising 40 BRPs were included. Body mass index, nonalcoholic fatty liver disease, psoriasis area and severity index, direct bilirubin level, indirect bilirubin level, and erythrocyte sedimentation rate level were identified as predictive factors of BRP. Nomogram models incorporating these factors demonstrated excellent discrimination capabilities with areas under the curve of 0.915 (95%CI, 0.846-0.916) in the training cohort and 0.933 (95%CI, 0.884-0.934) in the validation cohort. Calibration curves indicated good calibration for both cohorts, and decision curve analysis (DCA) revealed the excellent clinical utility of the predictive model. CONCLUSIONS: We developed the nomogram that integrated clinical features and laboratory parameters, providing a convenient and efficient method for predicting BRP risk.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".