Fatigue in patients with cancer receiving outpatient chemotherapy: a prospective two-center study
Bibliographic record
Abstract
BACKGROUND: Cancer-related fatigue (CRF) is one of the most common symptoms in patients with cancer. However, CRF has not been sufficiently evaluated as it involves various factors. In this study, we evaluated fatigue in patients with cancer receiving chemotherapy in an outpatient setting. METHODS: Patients with cancer receiving chemotherapy at the outpatient treatment center of Fukui University Hospital and Saitama Medical University Medical Center Outpatient Chemotherapy Center were included. The survey period was from March 2020 to June 2020. The frequency of occurrence, time, degree, and related factors were examined. All patients were asked to fill out the Edmonton Symptom Assessment System Revised Japanese version (ESAS-r-J) questionnaire, which is a self-administered rating scale, and patients with ESAS-r-J "Tiredness" scores of ≥ 3 were evaluated for factors related to tiredness, such as age, sex, weight, and laboratory parameters. RESULTS: A total of 608 patients were enrolled in this study. Fatigue after chemotherapy occurred in 71.0% of patients. ESAS-r-J "Tiredness" scores of ≥ 3 were observed in 20.4% of patients. The factors related to CRF were low hemoglobin level and high C-reactive protein level. CONCLUSIONS: Twenty percent of patients receiving cancer chemotherapy on an outpatient basis had moderate or severe CRF. Patients with anemia and inflammation are at increased risk of developing fatigue after cancer chemotherapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".