An Ecological Study of the Relationship between High Birthweight and Maternal Socioeconomic Indicators among US States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: While low birthweight babies are widely recognized as clinically challenging, large for gestational age (LGA) births also pose medical risks. To better understand and address the rise in LGA births in the USA, a better understanding of its population health determinants is indicated.OBJECTIVE: We aimed to measure associations between incidence rates of LGA births and (1) trends in maternal health insurance rates and (2) per capita state healthcare spending rates in US states.METHODS: Using public data from the CDC's Wide-ranging Online Data for Epidemiologic Research (WONDER) online natality database, the Current Population Survey of the United States Census Bureau, and the Centers for Medicare and Medicaid Services, we computed Pierson's correlation coefficient for rates of LGA births, the percentage of women without healthcare insurance, and state-level governmental spending on health care, across 50 states and the District of Columbia.RESULTS: There is substantial correlation between rates LGA incidence and the proportion of insured women in a state (r2=0.47) and moderate correlation with the extent of governmental healthcare spending (r2=0.17).
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it