Characteristics of Individuals with Moderate-to-Severe Traumatic Brain Injury and Predictors of Specialized Rehabilitation: A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract Purpose: Traumatic brain injury (TBI) is a disabling neurological condition that can cause substantial cognitive, behavioural, and physical health problems for the individual. Currently, it is a leading cause of death for Canadians. Rehabilitation (particularly specialized rehabilitation) has been shown to promote recovery in those with moderate-to-severe TBI, but not all eligible candidates receive it. We aim to 1) investigate demographic and clinical characteristics of individuals with moderate-to-severe TBI discharged to rehabilitation within 1-year post-injury over a 7-year period, and 2) identify predictors of discharge to specialized rehabilitation for these individuals. Materials and Methods: Patient characteristics were examined by linking their unique health insurance number through databases. Predictors of specialized rehabilitation were determined using logistic regression models. Results: Of 25,095 individuals with moderate-to-severe TBI, 4,748 individuals were admitted to rehabilitation within 365 days of injury between years 2010/2011 and 2017/2018. Most individuals who were admitted to rehabilitation were 64 years old or older (60%). Majority were male (65.6%). The most common cause of injury was related to a fall (61.7%). 13.9% of individuals had a mental health condition at the time of TBI hospitalization. 72.1% were discharged directly to rehabilitation following acute discharge. Mean wait time to rehabilitation was 37.3 (±52.5) days. 7.2% were rehospitalized immediately following rehabilitation discharge. Younger age, male sex, and higher rurality were some significant predictors of receiving specialized rehabilitation. Repatriated patients were less likely to receive specialized rehabilitation. Conclusion: This study identifies key healthcare utilization characteristics of individuals with moderate-to-severe TBI, as well as significant predictors of discharge to specialized rehabilitation for this population. We also highlight potential future research areas relating to these trends. This knowledge will be useful for policy planners and administrators who wish to improve patient access to care, appropriateness of care, and outcomes following moderate-to-severe TBI.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".