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 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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".