Factors associated with discharge destination from acute care after acquired brain injury in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The aim of this paper is to examine factors associated with discharge destination after acquired brain injury in a publicly insured population using the Anderson Behavioral Model as a framework. METHODS: We utilized a retrospective cohort design. Inpatient data from provincial acute care records from fiscal years 2003/4 to 2006/7 with a diagnostic code of traumatic brain injury (TBI) and non-traumatic brain injury (nTBI) in Ontario, Canada were obtained for the study. Using multinomial logistic regression models, we examined predisposing, need and enabling factors from inpatient records in relation to major discharge outcomes such as discharge to home, inpatient rehabilitation and other institutionalized care. RESULTS: Multinomial logistic regression revealed that need factors were strongly correlated with discharge destinations overall. Higher scores on the Charlson Comorbidity Index were associated with discharge to other institutionalized care in the nTBI population. Length of stay and special care days were identified as markers for severity and were both strongly positively correlated with discharge to other institutionalized care and inpatient rehabilitation, compared to discharge home, in both nTBI and TBI populations. Injury by motor vehicle collisions was found to be positively correlated with discharge to inpatient rehabilitation and other institutionalized care for patients with TBI. Controlling for need factors, rural location was associated with discharge to home versus inpatient rehabilitation. CONCLUSIONS: These findings show that need factors (Charlson Comorbidity Index, length of stay, and number of special care days) are most significant in terms of discharge destination. However, there is evidence that other factors such as rural location and access to supplemental insurance (e.g., through motor vehicle insurance) may influence discharge destination outcomes as well. These findings should be considered in creating more equitable access to healthcare services across the continuum of care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".