HG-106A NOVEL TOOL TO PREDICT THE SURVIVAL OF DIFFUSE INTRINSIC PONTINE GLIOMA PATIENTS: EXTERNAL VALIDATION OF THE SURVIVAL PREDICTION MODEL USING THE INTERNATIONAL DIPG REGISTRY
Bibliographic record
Abstract
PURPOSE: In this study, we aimed to determine the validity of the recently developed survival prediction model for DIPG through external validation in an independent patient cohort. Model performance was evaluated by analyzing the discrimination and calibration abilities. PATIENTS AND METHODS: The original survival prediction model was developed in a cohort of DIPG patients from the Netherlands, United Kingdom and Germany (n = 316). External validation was performed using patients from the International DIPG Registry, including patients from the United States, Canada, and Australia (n = 261). Basic comparison of the cohorts was performed using descriptive statistics and univariate or multivariable regression analyses. The generalizability of the original model was subsequently analyzed following a variety of analyses described previously by Royston et al. (2013). RESULTS: Baseline patient characteristics and results from the regression analyses in both cohorts were comparable. The slope on the prognostic index in the International Registry cohort was 0.72 (p < 0.01) and Harrell's c-index of concordance was 0.57. The chi2 to test model misspecification was 9.77 (p = 0.002). Kaplan-Meier curves from both cohorts show well-separated lines of low, intermediate and high-risk groups, which were confirmed by similar values for the hazard ratios across these risk groups. CONCLUSION: The current study demonstrates successful validation of the survival prediction model for DIPG, which is able to discriminate between patients with very short, average, and increased survival based on three clinical and one radiological variable.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".