The Potential Impact of Delayed Radiation Therapy on Patients with Glioblastoma
Bibliographic record
Abstract
BACKGROUND: Radiation therapy (RT) is the major component of glioblastoma treatment; however, the time to initiate RT after surgical intervention varies between institutions. Our study examined the time from diagnosis to the initiation of RT and its effects on overall patient survival. METHODS: We retrospectively examined 267 patients with glioblastoma who received RT as part of their therapy in two Canadian tertiary care centers. The primary goal of the study is to assess if time to RT can predict/impact survival in glioblastoma patients. RESULTS: The following variables were associated with an increased risk of death: hazard ratio (HR) of time to RT was 0.95 [95% confidence interval (CI), 0.91–0.99] for every extra week. HRs for the type of surgery (resection or biopsy) and type of management received (standard of care in comparison with RT regardless of chemotherapeutic agents other than concomitant and adjuvant temozolomide) were 0.50 (95% CI, 0.37–0.66) and 0.53 (95% CI, 0.38–0.75), respectively. HR for age was 1.02 (95% CI, 1.01–1.03) for every extra year. Standard 60 Gy RT HR was 0.70 [95% confidence interval (CI), 0.51–0.97] in younger patients. CONCLUSIONS: The time from diagnosis to the initiation of RT was found to be a significant prognostic factor for overall patient survival. The addition of temozolomide to the treatment protocol, age, standard RT dose in younger patients and extent of surgery are others factors associated with longer survival periods. Impact potentiel de la radiothérapie différée chez les patients atteints d'un glioblastome.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".