Bibliographic record
Abstract
OBJECTIVES: Birth season is related to a variety of later outcomes. Among them, mortality is of great interest because it represents lifetime health outcomes. We examined the relationship between birth season and mortality in the US. METHODS: We merged the US National Health Interview Survey (NHIS) and NHIS public-use linked mortality files and analyzed 17,082 men and 19,075 women who were followed for 20 years from 1986 to 2006. We used the Cox proportional hazards model to relate birth quarter to mortality, controlling for birth year fixed effects. RESULTS: After controlling for years of schooling and birth year fixed effects, we found that, relative to men born in the first quarter, men born in the fourth quarter were 11% less likely to die. For women, the benefit was the largest for women born in the third quarter who were 14% less likely to die than women born in the first quarter. In the relationship between birth season and mortality, cardiovascular diseases played a noticeable role for men and malignant neoplasms for women. CONCLUSIONS: These results were consistent with those for some developed countries, but not entirely with those for contemporary developing countries and developed countries of the past. Simple mechanisms based on the perinatal environment cannot account for the inconsistent results. We suggest that family background may play some, but not an exhaustive, role in the relationship between birth season and mortality. Am. J. Hum. Biol. 28:662-670, 2016. © 2016 Wiley Periodicals, Inc.
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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 | 0.004 |
| 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 | 0.001 |
| 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".