10.1016/s1084-3612(03)00049-2
Bibliographic record
Abstract
In seeking to learn more about the etiology and treatment of fatigue in patients with cancer, clinicians and researchers have been challenged to understand how fatigue can be distinguished from depression. Approaches currently used to study fatigue and depression in patients with cancer appear to be of limited usefulness in distinguishing these phenomena. This conclusion is supported by a review of studies in which the single-symptom and symptom-cluster approaches were used to measure fatigue and depression concurrently in patients with cancer. The review yielded consistent evidence of high positive correlations between fatigue and depression, even when attempts were made to eliminate overlapping item content. A consideration of causal mechanisms suggests why it remains difficult to distinguish between fatigue and depression. In addition to fatigue being a possible cause of depression and depression being a possible cause of fatigue, both fatigue and depression can share a common cause. That is, certain forms of cancer and cancer treatment can cause both fatigue and depression. These different mechanisms have implications for efforts to distinguish fatigue and depression and to identify appropriate treatments. For example, recently developed diagnostic criteria for a clinical syndrome of cancer-related fatigue might be useful in identifying fatigue that is caused by a major depressive disorder for which antidepressant therapy is generally indicated.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.989 | 0.990 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".