The relationship between the lunar cycle and epileptic attacks and their treatment
Bibliographic record
Abstract
Objective: The purpose of the present study was to evaluate the potential association between the phases of the moon and seizure attacks and treatment in patients diagnosed with epilepsy at a pediatric neurology clinic. Materials and Methods: 199 patients presenting to the Balıkesir University Medical Faculty pediatric neurology clinic, Turkey, diagnosed with epilepsy based on ILAE criteria were included in the study. The patients’ demographic characteristics, medications used, and family histories, and the frequency and duration of attacks were investigated retrospectively. Results: The mean age of the patients enrolled in the study (N=199) was 10.07±4.90 years. The patients were most frequently in the 12-18 age range (N=83, 41.7%). The majority of patients were male (N=104, 52.3%). Analysis revealed that seizures were most frequent in the full moon (N=54, 27.1%), followed by the new moon (N=52, 26.1%) and first quarter (N=47, 23.6%), and were least common in the third quarter (N=46, 23.1%). No statistically significant variation was determined in terms of attack frequencies during the different lunar phases between patients receiving monotherapy and polytherapy (p=0.206). Conclusion: The results of the present study suggest that there is no relationship between the lunar cycle and the frequency
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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".