Admission and discharge profiles of people with MS accessing in-patient rehabilitation in Canada
Bibliographic record
Abstract
Abstract Background Rehabilitation is the mainstay of management for people with disabilities of neurological origin to maximize function and reduce disability. Access to in-patient rehabilitation is usually reserved for people after crises or those who are discharged from acute care such as in stroke or trauma. Access to people with Multiple Sclerosis (MS) differs across countries and unknown for Canada. With the progression of MS, quality of life (QOL) becomes more closely coupled with independence and hence timely access to rehabilitation is important. The objectives of this paper are (i) to characterize the disability profiles of people with MS admitted to in-patient rehabilitation in Canada; and (ii) to estimate the extent to which disability profiles differ from admission to discharge. Methods A longitudinal study of a rehabilitation admission-to-discharge cohort of 3500 people with MS was conducting using latent class analysis on the five Functional Independence Measure (FIM) subscales for Self-care, Transfers, Locomotion, Bladder/Bowel, and Cognition. The extent to which age, sex, and calendar time was associated with latent class membership, at both admission and discharge, was estimated using ordinal logistic regression, and proportional odds model was calculated for each age and sex. Results A five-class model fit the data at admission and a four-class model fit the data at discharge determined using likelihood ratio G 2 , Akaike’s Information Criterion, and Bayesian Information Criterion. Conclusion At admission, the disability profiles showed a hierarchical progression across the FIM subscales. The least disabled profile was characterized by locomotion dependency only; the most disabled profile was characterized by dependencies in all subscales except cognition. At discharge, the least disabled class, representing 28% of discharges, was characterized by no dependencies; the most disabled class remained with dependencies (23%) in all areas. The study highlights the importance of reserving scarce rehabilitation services to those with more disability.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".