Patient-reported Symptom Burden, Rate of Completion of Palliative Radiotherapy and 30-day Mortality in Two Groups of Cancer Patients Managed With or Without Additional Care by a Multidisciplinary Palliative Care Team
Bibliographic record
Abstract
BACKGROUND/AIM: The aim of this study was to analyze differences in symptom burden, baseline and outcome parameters, including completion of palliative radiotherapy and 30-day mortality, between patients treated with palliative radiotherapy (RT) who were managed exclusively by regular oncology staff or a multidisciplinary palliative care team (MPCT) in addition. PATIENTS AND METHODS: This was a retrospective single-institution analysis. Comparison of two groups of patients: MPCT versus none (n=36 and 65, respectively). All patients provided Edmonton symptom assessment system (ESAS) data before RT. RESULTS: The MPCT group included significantly more patients with reduced performance status. Furthermore, these patients had higher ESAS symptom scores, except for two items (dyspnea, sleep). The largest differences were observed for pain, fatigue, anxiety and depression. The significant difference in pain scores was also reflected in different opioid medication rates. Failure to complete radiotherapy was more common in the MPCT group (11 and 2%, respectively, p=0.05). Thirty-day mortality was different, too (28 and 2%, respectively, p=0.0001). The Kaplan-Meier survival curves were not significantly different (1-year survival rates 21 and 25%, respectively, p=0.27). CONCLUSION: The MPCT group was characterized by a higher symptom burden. Prognostic factors such as performance status were not balanced between the two groups. Despite this fact, actuarial overall survival was comparable. Given the high rate of 30-day mortality in the MPCT group, efforts to optimize criteria for initiation of radiotherapy are warranted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".