Inpatient Rehabilitation Care in Alberta: How Much Does Stroke Severity and Timing Matter?
Bibliographic record
Abstract
BACKGROUND: We examined the impact of stroke severity and timing to inpatient rehabilitation admission on length of stay (LOS), functional gains, and discharge destination. METHODS: Alberta inpatient stroke rehabilitation data between April 2013 and March 2017 were analyzed. We evaluated the impact of stroke severity, as measured by the Functional Independence Measure (FIM), on timing to inpatient rehabilitation, functional gains, LOS, and discharge destination. Further, we examined whether timing to inpatient rehabilitation impacted the latter three factors. RESULTS: The 2404 adults were subcategorized as mild (1237), moderate (1031), or severe (136) based on FIM at inpatient rehabilitation admission. Length of time to rehabilitation admission was not significantly (p = 0.232) different between stroke severities. Mean length of time (days) to rehabilitation admission was 19.79 (20.3 SD) for mild, 27.7 (35.7 SD) for moderate, and 37.70 (56.8 SD) for severe stroke. Mean FIM change for mild (M = 16.3, 9.9 SD) differed significantly (p = 5.1 × 10-9) from moderate (M = 30.4, 16.4 SD) and severe (M = 31.0, 25.7 SD) stroke. The mean LOS for mild stroke (M = 41.3, 31.9 SD) was significantly (p = 5.1 × 10-9) different from moderate stroke (M = 86.8, 76.4 SD) and severe stroke (M = 126.1, 104.2 SD). Time to inpatient rehabilitation admission showed a small, significant impact on FIM change (p = 1.4 × 10-9, partial η2 0.022) and LOS (p = 1.1 × 10-19, partial η2 0.042). Shorter times to rehabilitation admission and mild stroke were associated with discharging home without needing homecare. CONCLUSION: Stroke severity has a significant impact on the conduct of inpatient rehabilitation. Yet, despite suggestions shortening timing to rehabilitation should improve outcomes, the impact on functional gains and rehabilitation LOS was small.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".