Anaemia and other predictors of fatigue among patients on palliative therapy for advanced cancer.
Bibliographic record
Abstract
The association of anaemia and other predictors of fatigue was studied in cancer patients on palliative treatment. A cohort of 128 consecutive patients (61 men and 67 women, mean age 63.6 years; range 36-85) was interviewed using the Edmonton Symptom Assessment System (ESAS) questionnaire, with 11 items describing cancer-related symptoms in visual analogue scale (VAS). Routine haematological samples were analysed at the time of interview. Both univariate and multivariate analyses were used to assess the independent predictors of fatigue. Out of the 10 symptoms recorded, fatigue was the single most frequent, reported by 91.3% of the patients, followed by pain (74.8%), sleeplessness (78.0%) and depression (74.2%). Anaemia was a significant determinant of fatigue (p=0.040)(OR=5.09; 95% CI 1.013-25.647). Out of the symptoms recorded, fatigue was significantly associated with depression (p=0.035), loss of appetite (p=0.016), anxiety (p=0.050), and sleeplessness (p=0.016). Total wellbeing was negatively associated with fatigue (OR=0.48, 95% CI 0.011-0.020)(p=0.0001). In multivariate analysis, anaemia was the most powerful independent predictor of fatigue, with OR=38.27 (95% CI 2.62-559.19)(p=0.008), followed by sleeplessness (OR=14.06 95% CI 1.44-137.02 p=0.023) and loss of appetite (OR=10.30 95% CI 1.04-101.10, p=0.045). Fatigue was unrelated to sex or age, or to the type of cancer, or the treatment category. Fatigue was common among cancer patients on palliative care. The single most powerful independent explanatory factor of fatigue was anaemia, implicating a need for interventional studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".