Effects of Full-Moon Definition on Psychiatric Emergency Department Presentations
Bibliographic record
Abstract
Objectives. The lunar cycle is believed to be related to psychiatric episodes and emergency department (ED) admissions. This belief is held by both mental health professionals and the general population. Previous studies analyzing the lunar effect have yielded inconsistent results.Methods. ED records from two tertiary care hospitals were used to assess the impact of three different definitions of the full-moon period, commonly found in the literature. The full-moon definitions used in this study were 6 hours before and 6 hours after the full moon (a 12-hour model); 12 hours before and 12 hours after the full moon (a 24-hour model); and 24 hours before and after the day of the full moon (a 3-day model).Results. Different significant results were found for each full-moon model. Significantly fewer patients with anxiety disorders presented during the 12-hour and 24-hour models; however, this was not true of the 3-day model. For the 24-hour model, significantly, more patients presented with a diagnosis of personality disorders. Patients also presented with more urgent triage scores during this period. In the 3-day model, no significant differences were found between the full-moon presentations and the non-full-moon presentations.Conclusions. The discrepancies in the findings of full moon studies may relate to different definitions of “full moon.” The definition of the “full moon” should be standardized for future research.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".