Effects of the Lunar Cycle, Seasons and the Meteorological Factors on Peripheral Vertigo
Bibliographic record
Abstract
Objective: This study aimed to determine whether peripheral vertigo is related to the lunar cycle, the seasons, or meteorological factors, in patients who presented to the ear, nose, and throat clinic. Methods: All the patients, diagnosed with vertigo between January 2020 and January 2022, were identified through a retrospective review of our hospital database. The clinical and demographic data of the patients were recorded. Daily humidity (minimum, average, and maximum; %), daily temperature (minimum, average, and maximum; °C), daily average and maximum wind speed (m/min), daily air pressure (minimum and average maximum; hPa) and wind direction (degrees) values were noted. Also, the phases of the moon, i.e., first quarter, new moon, last quarter, and full moon periods were determined. Results: A total of 5,432 patients were included in the study. No statistically significant differences were noted among them with respect to the lunar cycle (p=0.233). However, patient density was found to increase in the winter months. Conclusion: This study concluded that the frequency of diseases is related to meteorological factors, nonetheless, no statistical relationship was found between the lunar cycle and the frequency of patient entries.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".