Secular pattern of aneurismal rupture with the lunar cycle and season
Bibliographic record
Abstract
Background The lunar cycle and seasons may be associated with rates of rupture of intracranial aneurysms, but the literature is mixed. Studies of the association between the lunar cycle and rates of aneurysm rupture used the eight qualitative moon phases. The purpose of this study was to assess any association of aneurysm rupture with the lunar cycle and with the season. Materials and methods We retrospectively reviewed all cases of subarachnoid haemorrhage secondary to ruptured intracranial aneurysm treated with endovascular coiling in our institution over a 10-year period. We included only cases with a known rupture date. We used the degree of illumination of the moon to quantitatively code the lunar cycle. Results A total of 212 cases were included in our analyses. The odds of aneurysm rupture were significantly greater ( p < 0.001) when the moon was least (new moon) and most (full moon) illuminated, as compared to the middle of the lunar cycle. The odds of rupture tended to be higher ( p = 0.059) in the summer, compared to autumn. Conclusions The odds of aneurysm rupture were greater when the moon was least illuminated (new moon) and most illuminated (full moon), compared to the middle of the lunar cycle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".