Severe Fatigue During the Palliative Treatment Phase of Cancer
Bibliographic record
Abstract
BACKGROUND: Because of a rise in incidence and more effective treatments, the prevalence of patients with metastatic cancer is increasing fast. When palliative treatment is aimed at maintaining or improving patients' quality of life, knowledge about severe fatigue is clinically relevant because of its debilitating effect, but at present this information is lacking. OBJECTIVE: This study investigated the prevalence of severe fatigue in patients with various incurable cancers and whether severe fatigue increased with further treatment lines and differed between various cancers and treatment modalities. In addition, a relationship between severe fatigue and other symptoms was examined. METHODS: Patients were asked to fill in the Checklist Individual Strength, European Organization of Research and Treatment of Cancer-Quality of Life Questionnaire C30, and the McGill Pain Questionnaire during palliative anticancer treatment, and hemoglobin levels were collected. RESULTS: Of all participating patients (n = 137), 47% were severely fatigued. Patients who received first line of treatment were significantly less often severely fatigued (40%) compared with patients who received further lines (60%). Significantly more severe fatigue was observed when patients had more pain, dyspnea, appetite loss, nausea, vomiting, and constipation. CONCLUSIONS: During the phase of palliative anticancer treatment, fatigue was the most common symptom, nearly half of the patients had severe fatigue increasing with further treatment lines. Various treatment-related symptoms were related to more severe fatigue. IMPLICATIONS FOR PRACTICE: As severe fatigue is significantly related to other symptoms of cancer and its treatment, the screening and treatment of these cancer-related symptoms should be more stringent, as they might negatively influence each other.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".