Donepezil for Cancer Fatigue: A Double-Blind, Randomized, Placebo-Controlled Trial
Bibliographic record
Abstract
PURPOSE: To evaluate the effectiveness of donepezil compared with placebo in cancer patients with fatigue as measured by the Functional Assessment for Chronic Illness Therapy-Fatigue (FACIT-F). PATIENTS AND METHODS: Patients with fatigue score >or= 4 on a scale of 0 to 10 (0 = no fatigue, 10 = worst possible fatigue) for more than 1 week were included. Patients were randomly assigned to receive donepezil 5 mg or placebo orally every morning for 7 days. A research nurse contacted the patients by telephone daily to assess toxicity and fatigue level. All patients were offered open-label donepezil during the second week. FACIT-F and/or the Edmonton Symptom Assessment System (ESAS) were assessed at baseline, and days 8, 11, and 15. The FACIT-F fatigue subscale score on day 8 was considered the primary end point. RESULTS: Of 142 patients randomly assigned to treatment, 47 patients in the donepezil group and 56 in the placebo group were assessable for final analysis. Fatigue intensity improved significantly on day 8 in both donepezil and placebo groups. However, there was no significant difference in fatigue improvement by FACIT-F (P = .57) or ESAS (P = .18) between groups. In the open-label phase, fatigue intensity continued to be low as compared with baseline. No significant toxicities were observed. CONCLUSION: Donepezil was not significantly superior to placebo in the treatment of cancer-related fatigue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".