Relationships Among Pain, Fatigue, Insomnia, and Gender in Persons With Lung Cancer
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To examine the relationships among pain, fatigue, insomnia, and gender while controlling for age, comorbidities, and stage of cancer in patients newly diagnosed with lung cancer within 56 days of receiving chemotherapy. DESIGN: Secondary data analysis. SETTING: Accrual from four sites: two clinical community oncology programs and two comprehensive cancer centers. SAMPLE: 80 patients newly diagnosed with lung cancer. METHODS: Analysis from baseline observation of a randomized clinical intervention trial. Multinomial log-linear modeling was performed to explain the relationships among pain, fatigue, insomnia, and gender. MAIN RESEARCH VARIABLES: Pain, fatigue, insomnia, and gender. FINDINGS: For all people with lung cancer, fatigue (97%) and pain (69%) were the most frequently occurring symptoms; insomnia occurred 51% of the time. A model containing all main effects (two-way interactions of pain and fatigue, pain and insomnia, and insomnia and gender; and the three-way interaction of pain, fatigue, and insomnia, along with three covariates [age, comorbidities, and stage of cancer]) was a good fit to the data. Parameter estimates indicated that a statistically significant effect from the model was the three-way interaction of pain, fatigue, and insomnia. Gender did not make a difference. Age, comorbidities, and stage of cancer were not significant covariates. CONCLUSIONS: For people newly diagnosed with lung cancer undergoing chemotherapy, multiple symptoms occur simultaneously rather than in isolation; a symptom cluster exists, consisting of pain, fatigue, and insomnia; and no relationship was found among gender, pain, fatigue, and insomnia. IMPLICATIONS FOR NURSING: By understanding this symptom cluster, healthcare providers can target specific troublesome symptoms to optimize symptom management and achieve the delivery of high-quality cancer care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".