Death-related distress in adult primary brain tumor patients
Bibliographic record
Abstract
BACKGROUND: A diagnosis of cancer may increase mortality salience and provoke death-related distress. Primary brain tumor (PBT) patients may be at particular risk for such distress given the certainty of tumor progression, lack of curative treatments, and poor survival rates. This study is the first to examine the prevalence of death-related distress and its correlates in PBT patients. METHODS: Adult PBT patients (N = 105) enrolled in this cross-sectional study and completed the Death Distress Scale (subscales: Death Depression, Death Anxiety, Death Obsession), Generalized Anxiety Disorder-7, and Patient Health Questionnaire-9. Prevalence and predictors of death-related distress, and the relationships of demographic variables to clusters of distress, were explored. RESULTS: The majority of PBT patients endorsed clinically significant death-related distress in at least one domain. Death anxiety was endorsed by 81%, death depression by 12.5%, and death obsession by 10.5%. Generalized anxiety was the only factor associated with global death-related distress. Cluster analysis yielded 4 profiles: global distress, emotional distress, resilience, and existential distress. Participants in the resilience cluster were significantly further out from diagnosis than those in the existential distress cluster. There were no differences in cluster membership based on age, sex, or tumor grade. CONCLUSIONS: PBT patients appear to have a high prevalence of death-related distress, particularly death anxiety. Further, 4 distinct profiles of distress were identified, supporting the need for tailored approaches to addressing death-related distress. A shift in clusters of distress based on time since diagnosis also suggest the need for future longitudinal assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".