Can the prognosis of individual patients with glioblastoma be predicted using an online calculator?
Bibliographic record
Abstract
BACKGROUND: In an exploratory subanalysis of the European Organisation for Research and Treatment of Cancer and National Cancer Institute of Canada (EORTC/NCIC) trial data, Gorlia et al. identified a variety of factors that were predictive of overall survival, including therapy administered, age, extent of surgery, mini-mental score, administration of corticosteroids, World Health Organization (WHO) performance status, and O-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. Gorlia et al. developed 3 nomograms, each intended to predict the survival times of patients with newly diagnosed glioblastoma on the basis of individual-specific combinations of prognostic factors. These are available online as a "GBM Calculator" and are intended for use in patient counseling. This study is an external validation of this calculator. METHOD: One hundred eighty-seven patients from 2 UK neurosurgical units who had histologically confirmed glioblastoma (WHO grade IV) had their information at diagnosis entered into the GBM calculator. A record was made of the actual and predicted median survival time for each patient. Statistical analysis was performed to assess the accuracy, precision, correlation, and discrimination of the calculator. RESULTS: The calculator gives both inaccurate and imprecise predictions. Only 23% of predictions were within 25% of the actual survival, and the percentage bias is 140% in our series. The coefficient of variance is 76%, where a smaller percentage would indicate greater precision. There is only a weak positive correlation between the predicted and actual survival among patients (R(2) of 0.07). Discrimination is inadequate as measured by a C-index of 0.62. CONCLUSIONS: The authors would not recommend the use of this tool in patient counseling. If departments were considering its use, we would advise that a similar validating exercise be undertaken.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".