Effect of Early Palliative Care on Quality of Life in Patients with Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Background: Patients with metastatic non-small-cell lung cancer (NSCLC) experience great pain and stress. Our study aimed to explore the effect of early palliative care on quality of life in patients with NSCLC. Methods: A total of 150 patients were randomly divided into two groups: control group with conventional care and study group with early palliative care. The quality of life (QOL) rating scale and self-rating scale of life quality (SSLQ) were used to analyze the patients’ quality of life. The Hospital Anxiety and Depression Scale-D/A (HADS-D/A) and Patient Health Questionnaire 9 (PHQ-9) were used to analyze the patients’ mood. Pulmonary function indexes of peak expiratory flow (PEF), functional residual capacity (FRC), and trachea-esophageal fistula 25% (TEF 25%) were analyzed using the lung function detector. Results: The QOL and SSLQ scales scores of patients receiving early palliative care were significantly higher than those in the control group (p < 0.05). Moreover, the questionnaire results of the HADS-D/A and PHQ-9 were better in patients receiving palliative care than in the control group (p < 0.05 or p < 0.01). In addition, analytical results of pulmonary function showed that the levels of PEF, FRC, and TEF 25% in patients assigned to early palliative care were remarkably higher than those in the control group (p < 0.01 or p < 0.001). Conclusions: These data demonstrate that early palliative care improves life quality, mood, and pulmonary function of NSCLC patients, indicating that early palliative care could be used as a clinically meaningful and feasible care model for patients with metastatic NSCLC.
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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 | 0.002 |
| 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".