PO84WHOLE BRAIN RADIOTHERAPY (WBRT) FOLLOWING RESECTION OF BRAIN METASTASES: WHO DECIDES? AN AUDIT OF OUTCOMES AND CLINICIAN CONFIDENCE
Bibliographic record
Abstract
INTRODUCTION: Whole brain radiotherapy (WBRT) following brain metastases resection improves intra-cranial disease control without improving overall survival (OS). Expanding treatment options mean the role of WBRT should be individualised. We undertook a study to determine the rates and outcomes of post-op WBRT and the confidence of site-specific oncologists (e.g. breast, lung) in decision-making in this field. METHOD: Demographics and outcomes of patients who had brain metastases resected between 04/12 - 04/14 were retrospectively collected. Consultant opinion was collated via an on-line survey. RESULTS: 97 patients were identified (primary sites: lung 34%, breast 24%, colorectal 14%, renal 7%, melanoma 4%, others 17%). 68 (70%) underwent WBRT; it was omitted in 29 patients (21: too unwell/progressive disease, 5: previous cranial RT, 2: post-op chemo, 1: unknown). Median time to WBRT: 5.7 weeks, range 1-15.7 weeks. WBRT commenced >6 weeks post-op in 34%, with variation between disease groups. Median OS was 12.2 and 4.8 months in the WBRT and non-WBRT groups respectively. Intracranial progression was confirmed on imaging in 32% of patients in the WBRT group (73% local recurrence, 27% distant) and 45% in the non-WBRT group (69% local, 31% distant). For the whole group, 12 month OS was 40.8%, 24 month 21.5%. 78% (28/36) of survey respondents thought that decisions on WBRT use should be made by the neuro-oncology MDT. Only 14% felt completely confident in decision making and 11% very familiar with evidence in this field. CONCLUSION: WBRT is rarely electively withheld in our unit. There is scope to improve post-resection decision-making, pathways and education. Neuro-oncology MDTs play an important role in in this area.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".