Patterns of the Demographics, Clinical Characteristics, and Resource Utilization Among Maternal Decedents in Texas, 2001 - 2010: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Contemporary reporting of maternal mortality is focused on single, mutually exclusive causes of death among a minority of maternal decedents (pregnancy-related deaths), reflecting initial events leading to death. Although obstetric patients are susceptible to the lethal effects of downstream, more proximate contributors to death and to conditions not caused or precipitated by pregnancy, the burden of both categories and related patients' attributes is invisible to clinicians and healthcare policy makers with the current reporting system. Thus, the population-level demographics, clinical characteristics, and resource utilization associated with pregnancy-associated deaths in the United States have not been adequately characterized. METHODS: We used the Texas Inpatient Public Use Data File to perform a population-based cohort study of the patterns of demographics, chronic comorbidity, occurrence of early maternal demise, potential contributors to maternal death, and resource utilization among maternal decedents in the state during 2001 - 2010. RESULTS: There were 557 maternal decedents during study period. Chronic comorbidity was reported in 45.2%. Most women (74.1%) were admitted to an ICU. Hemorrhage (27.8%), sepsis (23.5%), and cardiovascular conditions (22.6%) were the most commonly reported potential contributing conditions to maternal death, varying across categories of pregnancy-associated hospitalizations. More than one condition was reported in 39% of decedents. One in three women died during their first day of hospitalization, with no significant change over the past decade. The mean hospital length of stay was 7.9 days and total hospital charges were $250,000 or higher in 65 (11.7%) women. CONCLUSIONS: The findings of the high burden of chronic illness, patterns of occurrence of a broad array of potential contributing conditions to pregnancy-associated death, and the resource-intensive needs of a large contemporary population-based cohort of maternal decedents may better inform preventive and intervention measures at the bedside and as healthcare policy priorities. The prevalent and unchanged occurrence of rapid maternal demise following presentation for hospitalization supports a special focus on means to identify and effectively address front-line clinician- and healthcare system-related performance areas that can improve maternal outcomes. The common reporting of more than one potential contributing condition underscores the complexity of determination of causes of maternal death.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".