Pharmacologic Interventions for Fatigue in Cancer and Transplantation: A Meta-Analysis
Bibliographic record
Abstract
Background: Our objective was to determine whether, compared with control interventions, pharmacologic interventions reduce the severity of fatigue in patients with cancer or recipients of hematopoietic stem-cell transplantation (hsct). Methods: For a systematic review, we searched medline, embase, the Cochrane Central Register of Controlled Trials, cinahl, and Psychinfo for randomized trials of systemic pharmacologic interventions for the management of fatigue in patients with cancer or recipients of hsct. Two authors independently identified studies and abstracted data. Methodologic quality was assessed using the Cochrane Risk of Bias tool. The primary outcome was fatigue severity measured using various fatigue scales. Data were synthesized using random-effects models. Results: = 48). Fatigue was significantly reduced with erythropoietin [standardized mean difference (smd): -0.52; 95% confidence interval (ci): -0.89 to -0.14] and with methylphenidate (smd: -0.36; 95% ci: -0.56 to -0.15); modafinil (or armodafinil) and corticosteroids were not effective. Conclusions: Erythropoietin and methylphenidate significantly reduced fatigue severity in patients with cancer and in recipients of hsct. Concerns about the safety of those agents might limit their usefulness. Future research should identify effective interventions for fatigue that have minimal adverse effects.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".