Reproduction of Original Glioblastoma and Brain Metastasis Research Findings Using Synthetic Data
Bibliographic record
Abstract
OBJECTIVE: Synthetic data (SD) is artificially generated information that mimics the statistical characteristics and correlations of real-world data, enabling researchers to simulate variables that are challenging to obtain in routine practice while overcoming confidentiality barriers. This study aims to evaluate the utility, validity, and potential limitations of SD in glioblastoma (GBM) and brain metastases (BM) research. METHODS: Three published neuro-oncology studies focusing on prognostic factors were selected: 2 involving GBM patients and 1 with BM patients. These studies were replicated using the MDClone platform, a healthcare data exploration tool that enables the creation of SD. Real-world data and SD were compared across patient demographic and outcome variables using summary statistics, normality testing, and t-test as required. RESULTS: 452 GBM patients and 1320 BM patients were generated with SD. Among GBM patients, longer median overall survival was associated with younger age (age<50: 16.3 months [95% CI: 12.8-19.8]; age 50-59: 15.6 [95% CI: 13.1-18.1]; age 60-69: 13.9 [95% CI: 12.1-15.7]; age>70: 8.8 [95% CI: 7.4-10.2], P < 0.001), greater extent of resection (debulking: 16.8 months [95% CI 14.9-18.7] vs. biopsy: 10.9 months [95% CI: 9.6-12.3], P < 0.001), and higher serum albumin (sAlb) (sAlb<30 g/L: 7.0 months [95% CI: 4.8-9.3]; sAlb 30-40 g/L: 12.9 [95% CI: 11.6-14.1]; sAlb>40: 16.2 [95% CI: 13.4-19.1], P < 0.05). Among BM patients, lower systemic inflammation scores (neutrophil-lymphocyte-ratio, leukocyte-lymphocyte-ratio, platelet-lymphocyte-ratio, monocyte-lymphocyte-ratio, and C-reactive-protein/albumin-ratio) were associated with longer overall survival (P < 0.05). These results aligned with the findings reported in the literature. CONCLUSIONS: Integrating SD into clinical research offers potential for providing accurate predictive insights without compromising patient privacy.
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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 | 0.013 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".