Impact of Therapy on Recovery during Rehabilitation in Patients with Traumatic Spinal Cord Injury
Bibliographic record
Abstract
Evidence-based planning of rehabilitation interventions is important to improving cost efficiency while maintaining patient and system outcomes. This article aims to explore the relationship between rehabilitation therapy, functional outcome, bed utilization, and care costs after traumatic spinal cord injury (tSCI). A retrospective review of 262 persons with tSCI admitted to an inpatient rehabilitation facility from 2005-2012 was conducted. Treatment variables and outcome measures included rehabilitation length of stay (LOS), days to rehabilitation (onset), hours and intensity of therapy, and Functional Independence Measure (FIM). Polynomial regression models and generalized additive models were applied to explore the relationship between therapy hours and motor FIM change. Simulation modeling was used to assess the impact of hypothetically increasing therapy intensity. Patients were grouped by injury as: C1-4 American Spinal Injury Association (ASIA) Impairment Scale (AIS) A,B,C; C5-8 AIS A,B,C; T1-S5 AIS A,B,C; and AIS D. The sample was 85% male, mean age 45.9, median LOS 102 days, and mean therapy intensity 5.7 h/week. Motor FIM change was positively associated with total hours of therapy (β = 0.40, p < 0.0001) up to a certain time point, adjusted for age, gender, injury, complications, and rehabilitation onset. Hypothetically increasing therapy intensity by 50% and 100% resulted in average motor FIM efficiency gain ranging between 0.04-0.07 and 0.1-0.17, respectively, across injury groups. The hypothetical changes resulted in reductions in the average LOS and bed utilization rate, translating to cost savings of $20,000 and $50,000 (2011 CAD) for the +50% and +100% scenarios, respectively. The results highlight the importance of monitoring functional change throughout rehabilitation after tSCI and the need for customized therapeutic strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".