Finite mixture models : applications to length of stay for delivery hospitalizations
Bibliographic record
Abstract
In the United States (U.S.), childbirth is the most common reason for hospitalization, and the maternal mortality rate per 100,000 (2017-2018) is markedly elevated in the U.S. (17.4) compared to neighboring Canada (10), the United Kingdom (7), and Japan (5) (Trends in Maternal Mortality, 2000 to 2017: Estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division). These data, the increased focus on addressing severe maternal morbidity and mortality to improve patient outcomes and reduce healthcare costs is well deserved. These women often have a longer delivery length of stay (LOS) and experience complications of varying severity. Patient and hospital characteristics influence LOS, but the right-skewness and heteroskedasticity of the LOS distribution for delivery hospitalizations makes modeling the LOS distribution difficult as assumptions for conventional parametric models are often violated (e.g., normality). This dissertation presents a practical approach with new capabilities for improved modeling of delivery LOS. The longstanding debate regarding the appropriate LOS for delivery includes as evidence the benefits of discharge to the mother’s physical and emotional health. However, early discharge can increase the risk of adverse events. In the U.S., most delivery stays last two or three days; those who remain inpatient for longer represent an important group which may have experienced severe maternal morbidity. Although a longer LOS may not be avoidable for women who have experienced complications, the concomitant costs and certain risks, increase with each day in the hospital. To improve the quality of maternal care, allocation of healthcare resources, and reduce costs, it is important to determine patient and hospital risk factors for extended delivery LOS. However, the challenge of modeling must be overcome to provide meaningful insights regarding predictors of delivery LOS. The strongly skewed distribution of LOS poses problems for modeling and analysis. Various methods and models, such as data transformations, have been examined for describing the LOS distribution, but are not always satisfactory in fitting the entire LOS distribution. Finite mixture models have been shown to be beneficial, as they accommodate the skewed LOS distribution without the need to transform the data or arbitrarily define the longer LOS outliers. These models allow all observations to be used and can identify the proportion of women staying longer. Finite mixture models decompose the LOS distribution into multiple underlying subpopulations. For example, delivery hospitalizations can be comprised of two subpopulations, one group staying shorter and another group staying longer.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".