Real‐world data on patterns and outcomes of radiation therapy for brain metastases in a population‐based cohort of lung cancer patients in Victoria
Bibliographic record
Abstract
INTRODUCTION: We evaluated real-world data on the patterns and outcomes of radiotherapy (RT) for brain metastases (BM) in a population-based cohort of patients with lung cancer (LC) in Victoria. METHODS: The Victorian Radiotherapy Minimum Data set (VRMDS) and the Victorian Cancer Registry (VCR) were linked to identify patients with LC who underwent RT for BM between 2013 and 2016. We determined: (i) proportion of patients treated with stereotactic radiosurgery (SRS); (ii) overall survival (OS); and (iii) 30-day mortality (30M) following RT for BM. RESULTS: Of the 1001 patients included in the study, 193 (19%) had SRS. There was no significant increase in SRS use over time - from 18% in 2013 to 21% in 2016 (P-trend = 0.8). In multivariate analyses, increased age (P = 0.03) and treatment in regional centres (P < 0.001) were independently associated with lower likelihood of SRS treatment. The median OS following RT for BM was 3.6 months. Patients who had SRS had better OS than those who did not have SRS (median OS 8.9 months vs. 3 months, P < 0.01). SRS use, age, sex and year of treatment were independently associated with OS in multivariate analyses. A total of 184 (18%) patients died within 30 days of RT for BM, and the proportion was higher in older (P = 0.001) and male patients (P = 0.004). CONCLUSION: One-in-five LC patients who received RT for BM had SRS. The improved OS with SRS is likely confounded by patient selection. It is important to reduce 30M by better selecting patients who may not benefit from RT for BM.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".