Critical Issues for Patients and Caregivers in Neuro-Oncology during the COVID-19 Pandemic: What We Have Learnt from an Observational Study
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic affected neuro-oncological patients and their caregivers regarding tumor care and emotional functioning, including Quality of Life (QoL). This study aimed to understand how COVID-19 affected their psychological state and the relations between patients and health personnel in neuro-oncology. METHODS: A cross-sectional study was conducted on neuro-oncological patients and their caregivers. RESULTS: A total of 162 patients and 66 caregivers completed the questionnaire. Altogether, 37.5% of patients perceived a greater risk of contracting COVID-19 compared to the general population. On a 0-10 scale, the patients' tumor-related anxiety score was 5.8, and their COVID-19-related score was 4.6. The caregivers reported 7.7 and 5.5, respectively. QoL was described as at least good in 75% of both patients and caregivers; the caregivers' care burden increased in 22.7% of cases during the pandemic, with no correlation with QoL. Future perception often changed, both in patients and caregivers. In 18% of cases, the cancer treatment schedule was changed, either by patient decision or by medical decision. However, 93.5% of patients were satisfied with their overall care. CONCLUSIONS: A considerable proportion of patients and caregivers still perceived the tumor disease as more burdensome than the pandemic, and their future as more uncertain. Such data suggest the need to build a productive alliance between patients and health professionals.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".