Relationships Among Neonatal Mortality, Hospital Volume, Weekday Demand, and Weekend Birth
Bibliographic record
Abstract
Background Research findings indicate that hospital volume affects the quality of care, that quality and demand for care on weekends differs from weekdays, and that giving birth on the weekend increases odds of neonatal mortality. Purpose To explore relationships among neonatal mortality, hospital volume, weekday demand for services, and weekend birth and risk of neonatal mortality. Methods For this retrospective, population-based, cohort study design, data were obtained from 32,140 electronic birth certificate records matched with 92 death certificate records from the Texas Department of State Health Services for 2012. Statistical analyses include descriptive procedures, analysis of variance, bivariate correlation, t-test, logistic regression, and chi-square tests of association. Results Higher hospital birth volume and higher concentrations of births during the week were associated with fewer neonatal deaths. Weekend births were associated not only with higher rates of neonatal death but also with lower birth weight and ethnicity of the mother. Conclusions These findings suggest the need for further study of the ways hospital-level organization of services and resources interact with individual risk factors to play a significant role in raising the neonatal mortality risk associated with weekend birth.
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.
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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".