Seasonality of Birth in Seasonal Affective Disorder
Bibliographic record
Abstract
BACKGROUND: Season of birth or seasonal changes in putative etiologic factors are thought to influence the development of several psychiatric illnesses. The aim of this investigation was to examine seasonal differences in the frequency of birth in a clinical sample of patients with seasonal affective disorder (SAD). METHOD: 553 outpatients suffering from SAD-DSM-IV-defined depressive disorder with winter-type seasonal pattern-who had been diagnosed and treated at the Department of General Psychiatry (University of Vienna, Austria) between 1994 and 2003, were included in this evaluation. We compared the observed number of births in our sample with expected values calculated from the general population. RESULTS: There was a significant deviation of the observed number of births from the expected values calculated on a monthly basis (p = .009). When comparing quarters (periods of 3 months), we found fewer births than expected in the first quarter of the year and a slight excess of births in the second and third quarters (p = .034). There were also more births in the spring/summer season and fewer than expected in fall and winter (p = .029). Interestingly, patients with melancholic depression were more frequently born in fall/winter and less often in spring/summer compared with patients with atypical depression (p = .008). CONCLUSION: Besides genetic factors, season of birth or seasonal changes in environmental factors also could influence the development of SAD. In addition, birth effects seem to be dependent on the symptom profile of the patients, but further studies are needed to elucidate the underlying mechanisms of these observations.
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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 | 0.001 | 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) | 0.001 | 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".