Effects of Melatonin on Appetite and Other Symptoms in Patients With Advanced Cancer and Cachexia: A Double-Blind Placebo-Controlled Trial
Bibliographic record
Abstract
PURPOSE: Prior studies have suggested that melatonin, a frequently used integrative medicine, can attenuate weight loss, anorexia, and fatigue in patients with cancer. These studies were limited by a lack of blinding and absence of placebo controls. The primary purpose of this study was to compare melatonin with placebo for appetite improvement in patients with cancer cachexia. PATIENTS AND METHODS: We performed a randomized, double-blind, 28-day trial of melatonin 20 mg versus placebo in patients with advanced lung or GI cancer, appetite scores ≥ 4 on a 0 to 10 scale (10 = worst appetite), and history of weight loss ≥ 5%. Assessments included weight, symptoms by the Edmonton Symptom Assessment Scale, and quality of life by the Functional Assessment of Anorexia/Cachexia Therapy (FAACT) questionnaire. Differences between groups from baseline to day 28 were analyzed using one-sided, two-sample t tests or Wilcoxon two-sample tests. Interim analysis halfway through the trial had a Lan-DeMets monitoring boundary with an O'Brien-Fleming stopping rule. Decision boundaries were to accept the null hypothesis of futility if the test statistic z < 0.39 (P ≥ .348) and reject the null hypothesis if z > 2.54 (P ≤ .0056). RESULTS: After interim analysis of 48 patients, the study was closed for futility. There were no significant differences between groups for appetite (P = .78) or other symptoms, weight (P = .17), FAACT score (P = .95), toxicity, or survival from baseline to day 28. CONCLUSION: In cachectic patients with advanced cancer, oral melatonin 20 mg at night did not improve appetite, weight, or quality of life compared with placebo.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".