Predictors for 30-Day Readmissions After Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To examine predictors for 30-day readmission post-onset of traumatic brain injury (TBI) after initial trauma hospitalization. DESIGN: Retrospective cohort. PARTICIPANTS: In total, 5284 patients with an acute TBI admitted from January 1, 2006, through December 31, 2015. METHODS: Demographic and clinical data after initial TBI onset were extracted from the local trauma registry and matched with the Dallas-Fort Worth Hospital Council registry. Multiple logistic regression analysis was used to determine factors significantly associated with 30-day readmission. Top diagnosis codes for 30-day readmission were also described. RESULTS: Patients were primarily male (64.6%), non-Hispanic White (47.6%), uninsured (35.4%), and aged 46.1 ± 23.3 years. In total, 448 patients (8.5%) had a 30-day readmission. Median cumulative charges for each readmitted subject was $34 313. Factors significantly associated with 30-day readmission were falling as the cause of injury, having increased Charlson Comorbidity Index and Injury Severity Score, and discharging to a skilled nursing facility or long-term acute care. Being uninsured was associated with decreased odds of a 30-day readmission. Top diagnosis codes among the readmission visits included cardiac codes (57.7%), fluid and acid-base disorders (54.8%), and hypertension (50.1%). CONCLUSION: These data highlight those at risk for 30-day readmission across a diverse population of TBI at a large medical center. Interventions such as health literacy education or patient navigation may help mitigate 30-day readmission for at-risk patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".