Predictors of Discharge Destination From Acute Care in Patients With Traumatic Brain Injury: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To systematically review studies on clinical and nonclinical predictors of discharge destination from acute care in patients with traumatic brain injury. METHODS: The search was conducted using 7 databases up to December 2016. A systematic review and in-depth quality synthesis were conducted on eligible articles that met the inclusion criteria. RESULTS: The search yielded 8503 articles of which 18 studies met the inclusion criteria. This study demonstrated that a larger proportion of patients with traumatic brain injury were discharged home. The main predictors of discharge to a setting with rehabilitation services versus home included increasing age, white and non-Hispanic race/ethnicity, having insurance coverage, greater severity of the injury, and longer acute care length of stay. Age was the only consistent factor that was negatively associated with discharge to inpatient rehabilitation facilities versus other institutions. CONCLUSION: Results of this study support healthcare providers in providing consultation to patients about the expected next level of cares while considering barriers that may helpful in effective discharge planning, decreasing length of stay and saving resources. These findings also suggest the need for further studies with a stronger methodology on the contribution of patients and families/caregivers to distinguish the predictors of discharge to dedicated rehabilitation facilities.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".