The Population Benefit of Radiotherapy for Malignant Brain Tumors: Local Control and Survival Estimates for Guideline-Based Use
Bibliographic record
Abstract
OBJECTIVE: To estimate the population benefit of radiotherapy (RT) for primary malignant brain tumors if evidence-based guidelines were routinely followed. METHODS: This study investigated 5-year local control (LC) and 2- and 5-year overall survival (OS) benefits. RT benefit was the absolute proportional benefit of RT alone over no RT for radical indications, and over surgery alone for adjuvant indications. Chemoradiotherapy (CRT) benefit was the absolute incremental benefit of concurrent chemotherapy and RT over RT alone. Decision tree models were adapted to define the incidence of each indication. Citation databases were systematically queried for the highest level of evidence defining indication benefits. Meta-analysis was performed if there were multiple sources of the same evidence level, and deterministic and probabilistic sensitivity analysis was also performed. RESULTS: Among all patients with malignant brain tumors, 82% had indications for curative- or adjuvant-intent RT. The magnitude of benefit was based on level I or II evidence in 44% of all patients. A total of 25 relevant studies were used to quantify indication benefits. All RT benefit included in the model was irreplaceable. For malignant brain tumors, the estimated population benefit for RT alone was 9% for 5-year LC (95% CI, 7%-10%), 9% for 2-year OS (95% CI, 8%-11%), and 5% for 5-year OS (95% CI, 4%-5%). The incremental benefit of CRT was 1% for 5-year LC (95% CI, 0%-2%), 7% for 2-year OS (95% CI, 4%-11%), and 3% for 5-year OS (95% CI, 1%-5%). The model was robust in sensitivity analysis. CONCLUSIONS: When optimally used, RT provides an important benefit for many patients with malignant brain tumors. The model provided a robust means for estimating the magnitude of this benefit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".