Effect of Month of Birth on Mean Birth Length in Austrian Newborns Born Between 1984 and 2021
Bibliographic record
Abstract
OBJECTIVE: The length of newborns shows seasonal fluctuations, but the patterns of these fluctuations vary greatly. This study analyses the fluctuation in birth length by birth month and temporal changes in Austrian newborns from the 1984 to 2021 birth cohorts. METHODS: A total of 2 317 927 singleton-term births between 1984 and 2021 in Austria were included in this retrospective population-based cohort study. A strict inclusion criterion was the Austrian citizenship of the mother. The effect of month of birth (MOB) on birth length was estimated using a multivariable linear model adjusting for maternal educational level, newborn sex, gestational age, year of birth (YOB) of the newborn, and parity of the mother. RESULTS: Newborn length varied by MOB, but there was also a temporal trend. In the birth cohorts up to 2004, the longest newborns were born in February, while from 2008 onward, the longest birth lengths were observed in the summer months. CONCLUSION: In this Austrian population-based sample, birth length shows nonrandom fluctuations by birth month. These patterns, however, varied considerably over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".