The association between changes in symptoms or quality of life and overall survival in outpatients with advanced cancer
Bibliographic record
Abstract
BACKGROUND: Several prognostic tools have been developed to aid clinicians in survival prediction. However, changes in symptoms are rarely included in established prognostic systems. We aimed to investigate the influence of changes in symptoms and quality of life (QOL) on survival time in outpatients with advanced cancer. METHODS: Study subjects included a subgroup of those with longitudinal symptom and QOL data within a larger, single-site parent study. We assessed patients' symptoms and QOL at enrollment and follow-up at an approximately 3-month interval. Patients' symptoms were evaluated by the Korean version of the Edmonton Symptom Assessment System (K-ESAS). QOL was checked by the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30). Participants were categorized into three groups by changes in symptoms or QOL. These groups were: improved (having at least a one level of improvement in the response scale), stable (no change), or worsened (at least a one level of worsening in the scale). We compared survival time in the improved plus stable vs. worsened groups, using a log-rank test. RESULTS: We analyzed 60 patients, with a median survival time of 346 days. In the Worsened group, depression (P<0.01) and sleep disturbance (P<0.01) by K-ESAS, and dyspnea (P<0.03) per the EORTC QLQ-C30, were statistically significantly related to shorter survival time compared to 'improved and stable' group. There was no relationship between changes in other symptoms, overall QOL, and survival. CONCLUSIONS: Longitudinal assessment of depression, sleep disturbance and dyspnea may be useful in prognostication of patients with advanced cancer. Further studies are needed to confirm our findings with more consecutive assessments in diverse populations.
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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 | 0.000 |
| 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".