A Profile of Patients With Traumatic Brain Injury Within Home Care, Long-Term Care, Complex Continuing Care, and Institutional Mental Health Settings in a Publicly Insured Population
Bibliographic record
Abstract
OBJECTIVE: To describe the sociodemographic and clinical profile of people with traumatic brain injury (TBI) in home care, nursing homes, and complex continuing care settings in a national sample. METHODS: Cross-sectional study using available Resident Assessment Instrument (RAI 2.0 and RAI Home Care [HC]) national databases in Canada from 1996 to 2011. The profile of people with TBI was compared with patients with and without prespecified neurological conditions within each setting. PARTICIPANTS: Adults 18 years and older identified with TBI (n = 10 878) and adult patients with other neurological (n = 422 300) and non-neurological (n = 571 567) conditions. MAIN MEASURES: Demographic and clinical characteristics, functional characteristics, mood and behavior, and treatment and medication variables. Data from Canadian home care (RAI-HC), mental health (RAI-MH), nursing home, and complex continuing care facilities (RAI Minimum Data Set 2.0). RESULTS: Patients with TBI were significantly different on almost all items. They were among the youngest in care settings, and psychotropic drug use by this population was among the highest in at least 2 settings. CONCLUSION: These data can inform the planning for appropriate care and resources for patients with TBI in a range of settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".