Melatonin in the treatment of cancer: a systematic review of randomized controlled trials and meta‐analysis
Bibliographic record
Abstract
Most observational studies show an association between melatonin and cancer in humans. We conducted a systematic review of randomized controlled trials (RCTs) of melatonin in solid tumor cancer patients and its effect on survival at 1 yr. With the aid of an information specialist, we searched 10 electronic databases from inception to October 2004. We included trials using melatonin as either sole treatment or as adjunct treatment. Prespecified criteria guided our assessment of trial quality. We conducted a meta-analysis using a random effects model. We included 10 RCTs published between 1992 and 2003 and included 643 patients. All trials included solid tumor cancers. All trials were conducted at the same hospital network, and were unblinded. Melatonin reduced the risk of death at 1 yr (relative risk: 0.66, 95% confidence interval: 0.59-0.73, I2=0%, heterogeneity P<or=0.56). Effects were consistent across melatonin dose, and type of cancer. No severe adverse events were reported. The substantial reduction in risk of death, low adverse events reported and low costs related to this intervention suggest great potential for melatonin in treating cancer. Confirming the efficacy and safety of melatonin in cancer treatment will require completion of blinded, independently conducted RCTs.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.017 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".