Trajectories of Suffering in the Last Year of Life Among Patients With a Solid Metastatic Cancer
Bibliographic record
Abstract
BACKGROUND: Reducing suffering at the end of life is important. Doing so requires a comprehensive understanding of the course of suffering for patients with cancer during their last year of life. This study describes trajectories of psychological, spiritual, physical, and functional suffering in the last year of life among patients with a solid metastatic cancer. PATIENTS AND METHODS: We conducted a prospective cohort study of 600 patients with a solid metastatic cancer between July 2016 and December 2019 in Singapore. We assessed patients' psychological, spiritual, physical, and functional suffering every 3 months until death. Data from the last year of life of 345 decedents were analyzed. We used group-based multitrajectory modeling to delineate trajectories of suffering during the last year of a patient's life. RESULTS: We identified 5 trajectories representing suffering: (1) persistently low (47% of the sample); (2) slowly increasing (14%); (3) predominantly spiritual (21%); (4) rapidly increasing (12%); and (5) persistently high (6%). Compared with patients with primary or less education, those with secondary (high school) (odds ratio [OR], 3.49; 95% CI, 1.05-11.59) education were more likely to have rapidly increasing versus persistently low suffering. In multivariable models adjusting for potential confounders, compared with patients with persistently low suffering, those with rapidly increasing suffering had more hospital admissions (β=0.24; 95% CI, 0.00-0.47) and hospital days (β=0.40; 95% CI, 0.04-0.75) during the last year of life. Those with persistently high suffering had more hospital days (β=0.70; 95% CI, 0.23-1.17). CONCLUSIONS: The course of suffering during the last year of life among patients with cancer is variable and related to patients' hospitalizations. Understanding this variation can facilitate clinical decisions to minimize suffering and reduce healthcare costs at the end of life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".