Development and Validation of a Prognostic Risk Model for Patients with Advanced Melanoma Treated with Immune Checkpoint Inhibitors
Bibliographic record
Abstract
BACKGROUND: Risk stratification tools for patients with advanced melanoma (AM) treated with immune checkpoint inhibitors (ICI) are lacking. We identified a new prognostic model associated with overall survival (OS). PATIENTS AND METHODS: A total of 318 treatment naïve patients with AM receiving ICI were collected from a multi-centre retrospective cohort study. LASSO Cox regression identified independent prognostic factors associated with OS. Model validation was carried out on 500 iterations of bootstrapped samples. Harrel's C-index was calculated and internally validated to outline the model's discriminatory performance. External validation was carried out in 142 advanced melanoma patients receiving ICI in later lines. RESULTS: High white blood cell count (WBC), high lactate dehydrogenase (LDH), low albumin, Eastern Cooperative Oncology Group (ECOG) performance status ≥1, and the presence of liver metastases were included in the model. Patients were parsed into 3 risk groups: favorable (0-1 factors) OS of 52.9 months, intermediate (2-3 factors) OS 13.0 months, and poor (≥4 factors) OS 2.7 months. The C-index of the model from the discovery cohort was 0.69. External validation in later-lines (N = 142) of therapy demonstrated a c-index of 0.65. CONCLUSIONS: Liver metastases, low albumin, high LDH, high WBC, and ECOG≥1 can be combined into a prognostic model for AM patients treated with ICI.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".