Retrospective Analysis of Emotional Burden and the Need for Support of Patients and Their Informal Caregivers after Palliative Radiation Treatment for Brain Metastases
Bibliographic record
Abstract
Cancer burdens not only the patients themselves but also their personal environment. A few studies have already focused on the mental health and personal needs of caregivers of patients. The purpose of this retrospective analysis was to further assess the emotional burden and unmet needs for support of caregivers in a population of brain metastasis patients. In the time period 2013–2020, we identified 42 informal caregivers of their respective patients after palliative radiation treatment for brain metastases. The caregivers completed two standardized questionnaires about different treatment aspects, their emotional burden, and unmet needs for support. Involvement of psycho-oncology and palliative care was examined in a chart review. The majority of the caregivers (71.4%, n = 30) suffered from high emotional burden during cancer treatment of their relatives and showed unmet needs for emotional and psychosocial support, mostly referring to information needs and the involvement in the patient’s treatment decisions. Other unmet needs referred to handling personal needs and fears of dealing with the sick cancer patient in terms of practical care tasks and appropriate communication. Palliative care was involved in 30 cases and psycho-oncology in 12 cases. There is a high need for emotional and psychosocial support in informal caregivers of cancer patients. There might still be room for an improvement of psychosocial and psycho-oncological support. Care planning should cater to the emotional burden and unmet needs of informal caregivers as well. Further prospective studies in larger samples should be performed in order to confirm this analysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".