Are we better a decade later in the accuracy of survival prediction by palliative radiation oncologists?
Bibliographic record
Abstract
BACKGROUND: Clinician predicted survival (CPS) plays a crucial role in palliative care, informing physicians of appropriate treatment best suited to the patient. The primary objective of this study was to assess the accuracy of CPS of cancer patients referred for palliative radiotherapy. Secondary objectives included an analysis of factors predictive of accurate CPS, comparisons of the accuracy of survival predictions over subsequent clinic visits, and comparisons to the previous study in the Rapid Response Radiotherapy Program (RRRP) in 2005. METHODS: CPS was provided by one of four radiation oncologists from August 2014 to March 2017. Karnofsky Performance Status (KPS), primary cancer site, and sites of metastases were recorded. Date of death was retrieved from the Patient Care System (PCS) and Excelicare. Mean difference between actual survival (AS) and CPS was used to determine the accuracy of survival predictions. RESULTS: One-hundred seventy-two patients were included in the final analysis. Survival was largely overestimated (n=135, 78.5%), with CPS being overestimated by 19.0 weeks on average. KPS (P=0.2), primary cancer site (P=0.08), and various sites of metastases were not significantly related to CPS accuracy. Gender was significantly related to CPS accuracy after multivariable analysis (P=0.04), but was no longer significant after excluding prostate and breast cancer patients in multivariable analysis (P=0.2). The mean difference between AS and CPS did not significantly change over subsequent visits (P=0.5) and CPS accuracy decreased significantly compared to the previous RRRP study (P=0.04). CONCLUSIONS: The survival estimates provided by radiation oncologists are inaccurately overestimated. Further research should aim to increase the accuracy of CPS in order to improve patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".