Palliative whole brain radiotherapy: Predictors of prescribing 5 versus 10 fractions.
Bibliographic record
Abstract
219 Background: The optimal dose for palliative whole brain radiotherapy (WBRT) continues to be debated. Common regimens include 20 Gy in five and 30 Gy in 10 fractions. We aimed to identify factors associated with WBRT dose schedules, hypothesizing that clinical prediction of survival (CPS) would influence prescribing practice. Methods: Demographic and clinicopathologic data were collected for consecutive patients with brain metastases receiving WBRT through a dedicated palliative radiation oncology clinic. At initial consultation, CPS were prospectively collected from treating radiation oncologists. Karnofsky performance status (KPS) and Mini-Mental Status Examination were available for 88.6% and 75.1%, respectively. Dose fractionation was collected and summary statistics calculated. Parameters were assessed for association with five fraction schedules using binary logistic regression, with odds ratios and 95% CI reported. Results: 193 patients underwent WBRT (N = 102 from 2010-2012; N = 91 from 2013-2014); 38/193 had 48 extracranial sites irradiated concurrently. 46.1% were male, mean age was 64.7 years (SD 11.6), and 63.7% had lung cancer. Median KPS was 70 (range 20-100) and median MMSE score was 27/30 (range 13-30). Median CPS and actual survival were 150 days (range 21-730d) and 96 days (range 11-1029d), respectively. 18.7% received WBRT within 30 days of death. 78.2% (151/193) and 17.6% (34/193) received five and 10 fractions, respectively; 8/193 were prescribed other schedules. On multivariate analysis, patients with KPS ≤ 70 were 5.93 times more likely to have received 5-fractions (95% CI 2.51-14.1; p < 0.0001). Those treated 2010-2012 were less likely to have received 5 fractions (OR 0.28; 95% CI 0.11-0.68; p = 0.005). CPS, age, gender, MMSE, histology, disease extent, and extracranial irradiation were not predictive of WBRT schedule. Conclusions: Patients treated with WBRT with KPS ≤70 and those treated more recently were more likely to receive five fractions. Oncologist CPS was not a statistically significant predictor of schedule in this cohort.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".