Bibliographic record
Abstract
BACKGROUND: Identify variables associated with intrapartum epidural use. METHODS: Odds ratios were calculated to quantify associations between selected variables and epidural use using a population-based case-control study of Washington State birth certificate data from 2009. RESULTS: Non-Whites had 10 - 45% lower odds of epidural use relative to Whites. Foreign-born women had 25 - 45% lower odds of epidural use compared to their US-born counterparts, except for Asians. Women who smoked or induced labor had higher roughly 2-fold higher odds of epidural use compared with non-smokers or women giving birth spontaneously, respectively. Women without a high school diploma or equivalent had lower odds of epidural use relative to those who graduated. Delivering at perinatal units, rural hospitals, or non-profit hospitals had ~50% lower odds of epidural use compared with secondary/teritiary perinatal units, urban hospitals or for-profit hospitals, respectively. CONCLUSION: Several individual and health service-related variables were associated with epidural use. These findings elucidate the clinical relevance of epidural use, and dispariaties in its utilization and in quality of care during delivery. KEYWORDS: Epidural use; Foreign birth; Labor; Racial disparities.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".