Impact of lymphopenia on survival for elderly patients with glioblastoma: A secondary analysis of the CCTG CE.6 (EORTC 26062-22061, TROG08.02) randomized clinical trial
Bibliographic record
Abstract
Abstract Background Lymphopenia may lead to worse outcomes for glioblastoma patients. This study is a secondary analysis of the CCTG CE.6 trial evaluating the impact of chemotherapy and radiation on lymphopenia, and effects of lymphopenia on overall survival (OS). Methods CCTG CE.6 randomized elderly glioblastoma patients (≥ 65 years) to short-course radiation alone (RT) or short-course radiation with temozolomide (RT + TMZ). Lymphopenia (mild-moderate: grade 1–2; severe: grade 3–4) was defined per CTCAE v3.0, and measured at baseline, 1 week and 4 weeks post-RT. Preselected key factors for analysis included age, sex, ECOG, resection extent, MGMT methylation, Mini-Mental State Examination, and steroid use. Multinomial logistic regression and multivariable Cox regression models were used to identify lymphopenia-associated factors and association with survival. Results Five hundred and sixty-two patients were analyzed (281 RT vs 281 RT+TMZ). At baseline, both arms had similar rates of mild-moderate (21.4% vs 21.4%) and severe (3.2% vs 2.9%) lymphopenia. However, at 4 weeks post-RT, RT+TMZ was more likely to develop lymphopenia (mild-moderate: 27.9% vs 18.2%; severe: 9.3% vs 1.8%; p<0.001). Developing any lymphopenia post-RT was associated with baseline lymphopenia (P < .001). Baseline lymphopenia (hazard ratio [HR] 1.3) was associated with worse OS (HR: 1.30, 95% confidence interval [CI] 1.05–1.62; P = .02), regardless of MGMT status. Conclusions Development of post-RT lymphopenia is associated with addition of TMZ and baseline lymphopenia and not with RT alone in patients treated with short-course radiation. However, regardless of MGMT status, only baseline lymphopenia is associated with worse OS, which may be considered as a prognostic biomarker for elderly glioblastoma patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".