Mental health, treatment preferences, advance care planning, location, and quality of death in advanced cancer patients with dependent children
Bibliographic record
Abstract
BACKGROUND: Clinicians observe that advanced cancer patients with dependent children agonize over the impact their death will have on their children. The objective of this study was to determine empirically whether advanced cancer patients with and without dependent children differ in treatment preferences, mental health, and end-of-life (EOL) outcomes. METHODS: Coping with Cancer is a National Cancer Institute/National Institute of Mental Health-funded, multi-institutional, prospective cohort study of 668 patients with advanced cancer. Patients with and without dependent children were compared on rates of psychiatric disorders, advance care planning (ACP), EOL care, quality of their last week of life, and location of death. RESULTS: In adjusted analyses, patients with advanced cancer who had dependent children were more likely to meet panic disorder criteria (adjusted odds ratio [AOR], 5.41; 95% confidence interval [95% CI], 2.13-13.69), more likely to be worried (mean difference in standard deviations [delta], 0.09; P=.006), and more likely to prefer aggressive treatment over palliative care (AOR, 1.77; 95% CI, 1.07-2.93). Patients with dependent children were less likely to engage in ACP (eg, do not resuscitate orders: AOR, 0.44; 95% CI, 0.26-0.75) and had a worse quality of life in the last week of life (delta, 0.15; P=.007). Among spousal caregivers, those with dependent children were more likely to meet criteria for major depressive disorder (AOR, 4.53; 95% CI, 1.47-14) and generalized anxiety disorder (AOR, 3.95; 95% CI, 1.29-12.16). CONCLUSIONS: Patients with dependent children were more anxious, were less likely to engage in ACP, and were more likely to have a worse quality of life in their last week of life. Advanced cancer patients and spousal caregivers with dependent children represent a particularly distressed group that warrants further clinical attention, research, and support.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".