Month-of-birth-effect in multiple sclerosis in Austria
Bibliographic record
Abstract
BACKGROUND: The month-of-birth-effect (MoBE) describes the finding that multiple sclerosis (MS) patients seem to have been born significantly more frequently in spring, with a rise in May, and significantly less often in autumn and winter with the fewest births in November. OBJECTIVES: To analyse if the MoBE can also be found in the Austrian MS population, and if so, whether the pattern is similar to the reported pattern in Canada, United Kingdom, and some Scandinavian countries. METHODS: The data of 7886 MS patients in Austria were compared to all live births in Austria from 1940 to 2010, that is, 7.256545 data entries of the Austrian birth registry and analysed in detail. RESULTS: Patterns observed in our MS cohort were not different from patterns in the general population, even when stratifying for gender. However, the noticeable and partly significant ups and downs over the examined years did not follow the distinct specific pattern with highest birth rates in spring and lowest birth rates in autumn that has been described previously for countries above the 49th latitude. CONCLUSION: After correcting for month-of-birth patterns in the general Austrian population, there is no evidence for the previously described MoBE in Austrian MS patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".