Melatonin’s Benefits and Risks as a Therapy for Sleep Disturbances in the Elderly: Current Insights
Bibliographic record
Abstract
Aging is accompanied by circadian changes, including disruptive alterations in the sleep/wake cycle, as well as the beginning of low-degree inflammation ("inflammaging"), a scenario that leads to several chronic illnesses, including cancer, and metabolic, cardiovascular, and neurological dysfunctions. As a result, any effective approach to healthy aging must consider both the correction of circadian disturbance and the control of low-grade inflammation. One of the most important prerequisites for healthy aging is the preservation of robust circadian rhythmicity (particularly of the sleep/wake cycle). Sleep disturbance disrupts various activities in the central nervous system, including waste molecule elimination. Melatonin is a chemical with extraordinary phylogenetic conservation found in all known aerobic creatures whose alteration plays an important role in sleep changes with aging. Every day, the late afternoon/nocturnal surge in pineal melatonin helps to synchronize both the central circadian pacemaker found in the hypothalamic suprachiasmatic nuclei (SCN) and a plethora of peripheral cellular circadian clocks. Melatonin is an example of an endogenous chronobiotic substance that can influence the timing and amplitude of circadian rhythms. Moreover, melatonin is also an excellent anti-inflammatory agent, buffering free radicals, down-regulating proinflammatory cytokines, and reducing insulin resistance, among other things. We present both scientific and clinical evidence that melatonin is a safe drug for treating sleep disturbances in the elderly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".