Intervention for Symptom Cluster Management of Fatigue, Loss of Appetite, and Anxiety among Patients with Lung Cancer undergoing Chemotherapy
Bibliographic record
Abstract
OBJECTIVE: Patients with lung cancer can experience various distressing symptoms. The present study aims to use symptom cluster management intervention based on symptom management theory to moderate the severity of symptom clusters, including fatigue, loss of appetite, and anxiety, in patients with lung cancer undergoing chemotherapy. METHODS: A quasi-experimental study was conducted using historical controls to assess and compare the effect of a novel symptom cluster management intervention on the severity of fatigue, loss of appetite, and anxiety in patients with lung cancer undergoing chemotherapy. Lung cancer patients were recruited from an outpatient chemotherapy unit at a university hospital in Thailand. Eighty participants were assigned equally to the experimental and control groups. The study outcomes, including fatigue, loss of appetite, and anxiety, were assessed with the Edmonton Symptom Assessment System at baseline and days 7, 14, and 28 postintervention. Repeated-measures ANOVA was analyzed to determine mean differences between groups across time. RESULTS: < 0.001) for all symptoms within the cluster indicate the benefit of the intervention over time. CONCLUSIONS: The pattern of changes in the symptom cluster across the study period was significantly different between the two study groups. Patients in the experimental group reported an improvement in fatigue, loss of appetite, and anxiety over time after receiving the intervention. The results suggested that the symptom cluster management intervention provided a promising approach for the simultaneous treatment of multiple symptoms within a cluster.
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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".