Effects of evidence-based nursing under a quantitative assessment strategy on cancer-related fatigue, self-management ability, and quality of life in lung cancer patients undergoing chemotherapy
Bibliographic record
Abstract
Objective: To investigate the effects of evidence-based nursing under a quantitative assessment strategy (EB-NQAS) on cancer-related fatigue (CRF), self-management ability, quality of life (QoL), and adverse events in lung cancer patients undergoing chemotherapy. Methods: A single-center, prospective, randomized controlled trial (RCT) was conducted, enrolling 150 lung cancer patients undergoing chemotherapy between January 2024 and January 2025. Participants were randomized into the intervention group (n=75) and control group (n=75) using a random number table. The intervention group received EB-NQAS, which featured personalized care plans based on symptom quantification via the Edmonton Symptom Assessment Scale (ESAS), while the control group received routine nursing care. Outcomes were assessed at baseline and 3 months. CRF was assessed using the Piper Fatigue Scale (PFS), self-management ability via the Adult Health Self-Management Scale (AHSMSRS), and QoL via the European Organization for Research and Treatment of Cancer Quality of Life Core Questionnaire (EORTC-QLQ-C30). Adverse events were recorded. Data were analyzed using linear mixed-effects models and chi-square tests. Results: Analyses were conducted on all 150 randomized participants per the intention-to-treat principle. After 3 months, the EB-NQAS group showed a significantly greater reduction in the total PFS score compared to the control group (22.88 ± 1.72 vs. 25.36 ± 1.63; group × time interaction, P<0.001). The intervention group also demonstrated significantly greater improvements in total AHSMSRS scores (146.00 ± 9.77 vs. 128.45 ± 12.45) and global health status/QoL scores (77.66 ± 9.74 vs. 68.91 ± 9.51) than the control group (all group × time interactions, P<0.001). The incidence of gastrointestinal reactions in the intervention group (5.33%) was lower than that in the control group (18.67%, P = 0.012). No significant differences were observed in other adverse events (P>0.05). Conclusion: Evidence-based nursing under a quantitative assessment strategy effectively reduces CRF, improves self-management ability and QoL, and decreases gastrointestinal reactions in lung cancer patients undergoing chemotherapy, demonstrating strong potential for clinical application.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".