Clinical prognostic factors affecting survival in patients with newly diagnosed Glioblastoma Multiforme (GBM)
Bibliographic record
Abstract
9599 Background:In the EORTC 26981–22981/NCIC CE3 phase III trial, 573 pts with newly diagnosed GBM were randomized to receive radiotherapy (RT) or RT plus Temozolomide (results separately reported). In this analysis we evaluate clinical parameters as prognostic factors for survival. Methods:The Cox Proportional Hazard model was used to assess factors related to patients' characteristics (age, gender, WHO Performance Status (PS), Mini Mental State Examination (MMSE)), disease history (extent of resection, time between surgery and start of radiotherapy, administration of corticosteroids, baseline haematological and non-haematological toxicity status), tumor location (hemisphere and lobe). These factors were first screened by univariate technique and the hemisphere was the only variable that did not pass the 10% significance level.Results: In the multivariate analysis, poorer prognosis was associated with the extent of surgery (biopsy worse than partial or complete debulking) (p<0.0001), greater age (p=0.016), male gender (p=0.013), lower MMSE Score (p<0.0001), the administration of corticosteroids (p=0.012), tumor location in the frontal lobe (p=0.002) or tumor on more than one lobe (p=0.02). The time between surgery and start of radiotherapy, the baseline haematological and non-haematological toxicity status and the WHO PS status were not retained. Gender seems related to overall survival but probably because the proportion of male patients with a tumor on more than one lobe was significantly higher (p<0.0001).Conclusions: This analysis confirms the extent of surgery and age as major prognostic factors. MMSE has a better correlation to survival than WHO PS. The model will be validated with the bootstrap technique and on an independent data set. The results should be taken into consideration for future EORTC glioma trials. Author Disclosure Employment or Leadership Consultant or Advisory Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Schering-Plough
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".