Melatonin for delirium prevention in acute medically ill, and perioperative geriatric patients
Bibliographic record
Abstract
Delirium is a challenging neuropsychiatric ailment that has a negative impact on morbidity and mortality and is difficult to treat once it has developed. The purpose of this review was to analyze the efficacy of melatonin in the prevention of delirium in hospitalized geriatric patients in the acute medically ill and perioperative wards. The databases searched included PubMed (1946 to February 12, 2020), CINAHL (1982 to February 12, 2020), EMBASE (1974 to February 12, 2020), and Web of Science (1900 to February 12, 2020) using search terms related to melatonin, delirium, and prevention. Meta-analyses, randomized controlled trials, and observational studies were included. We excluded publications pertaining to the intensive care unit or oncology, case reports/series, and those not in English. Seven full-text publications were included for qualitative analysis. Patient comorbidities, patient medications, melatonin dosing, dosing regimens, and duration of treatment varied between the studies, which yielded heterogeneous results. Overall, this literature review yielded four studies that showed positive results and three that showed negative results for delirium prevention. The current data for the use of melatonin in delirium is conflicting. This area requires further research of more homogeneous studies with larger sample sizes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".