Contribution of Patient-reported Symptoms Before Palliative Radiotherapy to Development of Multivariable Prognostic Models
Bibliographic record
Abstract
BACKGROUND/AIM: Typically, prognostic scores predicting survival in patients with metastatic cancer are based on disease- and patient-related factors, such as extent of metastases, age and performance status. Patient-reported symptoms have been included less often. Our group has assessed all patients with the Edmonton Symptom Assessment System (ESAS, a one-sheet questionnaire addressing 11 major symptoms and wellbeing on a numeric scale of 0-10) before palliative radiotherapy (PRT) since 2012. Therefore, we were able to analyze the prognostic impact of baseline ESAS symptom severity. PATIENTS AND METHODS: We performed a retrospective review of 102 patients treated with PRT between 2012 and 2015. All ESAS items were analyzed by two different methods, dichotomized by median score and by score <4 vs. ≥4. Uni- and multivariable analyses were performed to identify prognostic factors for survival, and from these a 4-tiered score was developed. RESULTS: The most common tumor types were prostate, breast and non-small cell lung cancer, predominantly with distant metastases. Despite differences between the two methods of ESAS data handling, the final multivariable models were strikingly similar. Therefore, the better reproducible cut-off was chosen, i.e. a score ≥4. Multivariable analyses resulted in 4 significant prognostic factors, which contributed equally to the 4-tiered survival score (performance status, more than one cancer diagnosis, progressive disease outside the PRT target volume(s), ESAS appetite). Estimated median survival for different point sums was 24.5 months (0), 8.4 months (1), 4.7 months (2) and 3.0 months (3), p=0.0001. CONCLUSION: This score identified patients with different survival outcomes, including a good prognostic group with median survival of approximately 2 years. The results may be useful to inform PRT fractionation.
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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 | 0.030 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".