Quality of life and survival prediction in terminal cancer patients
Bibliographic record
Abstract
BACKGROUND: It remains unclear whether health-related quality of life (HRQoL) measurements from patients and staff can be combined with medical data to predict survival in patients with terminal cancer. METHODS: The correlations between survival and potential health-related quality-of-life (HRQoL) prognostic variables were explored in 2 independent cohorts of patients with terminal cancer (248 patients in Cohort 1 and 756 patients in Cohort 2) after adjusting for clinical and demographics variables using Cox regression models. RESULTS: At the onset of the terminal phase (Cohort 1), the hazards of dying increased by 28% in the presence of dyspnea and by 68% in the presence of nausea/emesis; however, the most important predictors of worse survival were the presence of liver metastases (hazard ratio [HR], 2.5; 95% confidence interval [95% CI], 1.8-3.8), lung tumor (HR, 2.4; 95% CI, 1.7-3.4), and tumor burden (HR, 2.0; 95% CI, 1.4-2.7). In contrast, for patients who were seen in later stages of their terminal disease (Cohort 2), dyspnea (HR, 1.5; 95% CI, 1.1-1.9) and the coexistence of weakness with a diagnosis of digestive tumors (HR, 5.2; 95% CI, 1.2-21.8), breast tumors (HR, 3.1; 95% CI, 1.6-6.2), and genitourinary tumors (HR, 3.5; 95% CI, 1.6-7.8) were more predictive of survival than the type of tumor primary. Emotional functioning along with anxiety, spiritual distress, and lack of insight were not associated consistently with survival in both cohorts. CONCLUSIONS: Health care professionals should focus on physical HRQoL indicators, such as nausea and emesis, dyspnea, and weakness, to gather prognostic clues in patients with terminal cancer. These symptoms may reflect consequences of cancer cachexia and the progress of patients toward this terminal syndrome. Psychosocial distress did not appear to be associated consistently with survival; however, future studies should clarify further the prognostic significance of "positive attitudes", such as hope and optimism, in patients with advanced cancer.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".