Rehabilitation after musculoskeletal injury: an overview of systems in the United States and Canada
Bibliographic record
Abstract
As North America is largely industrialized with a variety of available private transportation options, trauma is a common occurrence, resulting in significant burdens of disability and costs to the health care system. To meet increasing trauma care needs, there is a robust organization of trauma and rehabilitation systems, particularly within the United States and Canada. The American and Canadian health care systems share multiple similarities, including well-equipped Level I trauma centers, specialized inpatient rehabilitation units for polytrauma patients, and thorough evaluations for recovery and post-discharge placement. However, they also have several key differences. In Canada, the criteria for admission to inpatient rehabilitation vary by location, and inpatient rehabilitation is universally accessible, whereas outpatient rehabilitation services are generally not covered by insurance. In the United States, these admission criteria for post-acute inpatient rehabilitation are standardized, and both inpatient and outpatient services are covered by private and government-funded insurance with varying durations. Overall, both health care systems face challenges in post-acute rehabilitation, including benefit limitations and limited provider access in rural areas, and must continue to evolve to meet the rehabilitation needs of injured patients as they reintegrate into their communities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".