Symptom Burden in Patients Treated With Palliative Radiotherapy Before and During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND/AIM: Oncological care has faced several challenges during the COVID-19 pandemic, e.g. treatment delay and worsening symptoms. Patient-reported anxiety, depression and sleep quality might have changed due to these special circumstances. Therefore, we analyzed the symptom burden of patients treated with palliative radiotherapy at our center. PATIENTS AND METHODS: A retrospective study was performed of 50 consecutive patients and the results were compared to those obtained in a previous pre-COVID study. The Edmonton Symptom Assessment Scale was employed to assess the preradiotherapy symptoms. RESULTS: The highest mean scores were reported for pain in activity (3.2) and dry mouth (3.1). Regarding anxiety, sadness/depression and sleep, the corresponding scores were 1.5, 1.2 and 2.7, respectively. Compared to the previous study, no significant increases were found. Most items had numerically lower mean values, e.g. anxiety (1.5 vs. 2.7). Both study populations had comparable median age (70.5 vs. 70 years), gender distribution and proportion of patients with bone metastases. However, there were two significant imbalances, namely a lower proportion of patients with prostate cancer (12 vs. 30%, p=0.02) and breast cancer (0 vs. 12%, p=0.02). CONCLUSION: In patients who showed up for radiation treatment planning, the suspected increase in anxiety, sadness/depression and sleep disturbance was not demonstrable. It is not known whether or not patients with substantial worries chose to decline referral to palliative radiotherapy. Therefore, comprehensive large-scale studies of patterns of care are needed to fully understand the impact of COVID-19-related measures.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".