Does expected survival influence palliative radiotherapy treatment recommendations?
Bibliographic record
Abstract
35 Background: Survival is often overestimated, yet physicians rely on such predictions to recommend appropriate therapy and assist with end-of-life planning. Administration of radiotherapy (RT) within the last 30 days of life has been suggested as an indicator of poor quality care, since acute side effects reduce quality of life with insufficient time for symptomatic benefit. We investigated whether life expectancy predicted at the time of consultation correlates with palliative RT recommendations. Methods: Radiation oncologists from a dedicated palliative Radiation Oncology outpatient clinic anonymously completed survival estimations after clinical assessment, and recorded factors upon which each estimate was based. Demographics, primary histology, RT details, and date of death were abstracted. Summary statistics and Kaplan-Meier estimates of actual survival (AS) were obtained. Correlations between AS and clinical predictions of survival (CPS) were calculated using Spearman’s correlation coefficient (r). Multivariate logistic regression analysis explored factors associated with RT recommendations. Results: 476 survival predictions were made for 420 unique patients (06/2010-01/2014). Median age was 67.7 years, 61.9% were male and 44.0% had lung cancer. Karnofsky Performance Status (KPS) was > 70 at 23.9% of clinic visits. At 84.5% of consultations, RT was prescribed to 538 separate volumes (29.2% receiving 8Gy, 54.8% 20Gy, 6.3% 30Gy, 9.7% other). Mean AS was 179 days (SD 187d), moderately correlating with mean CPS of 242 days (SD 261d) with r = 0.38 (p < 0.0001). Factors most frequently cited as influencing CPS were KPS and extent of disease. At the time of 30/476 visits, CPS was < 30 days; at 19 of these visits, RT was prescribed to 26 volumes (21 bone, 3 whole brain, 2 chest), 2/3 as single fractions, finishing a median of 17 days before death. Expected survival was predictive of prescribed RT dose on univariate logistic regression, but did not retain significance on multivariate analysis. Conclusions: Despite international surveys in which prognosis has been cited as the main factor affecting treatment decisions, in this cohort, other aspects appear to have more strongly influenced palliative RT recommendations.
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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 | 0.004 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".