Risk predictors for hospital readmission in a low socio-economic and underserved population
Bibliographic record
Abstract
Objective: Hospital readmissions are significant and potentially preventable sources of healthcare cost in the United States. The Affordable Care Act (ACA) establishes the Hospital Readmissions Reduction Program (HRRP) in an attempt to reduce readmissions by penalizing institutions whose 30-day readmission rates are above the national average. The current study examines demographic and clinical variables associated with early hospital readmission in a low socioeconomic status, underserved population.Methods: A secondary data analysis was conducted of 2,536 patients from the acute primary care facilities who were hospitalized. Age, sex, race, ethnicity, smoking status, systolic blood pressure, diastolic blood pressure, body temperature, pulse rate, and days to follow up visit were analyzed in a sample of 2,536 hospitalized patients at or below 200% of federal poverty guidelines in Central Texas to determine association with risk of 0-30- (30), 31-60- (60) and 61-90- (90) day all-cause readmission.Results: Multinomial statistical analysis found pulse rate was associated with 30-, 60-, and 90-day readmission as compared to a control group. Days to follow-up were associated with decreased risk of readmission in all groups, and passive smoking status was associated with decreased risk of 90-day readmission as compared to a control group.Conclusions: Results offer healthcare providers with tools for potentially identifying patients at elevated risk for readmission in a disadvantaged population and suggest further investigation of other clinical and laboratory variables as predictors of readmission risk.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".