The Impact of Severity, Timing, and Sex on Outcomes of Inpatient Stroke Rehabilitation
Bibliographic record
Abstract
Stroke severity and time to rehabilitation admission are important factors in influencing stroke rehabilitation outcomes. We aimed to determine the impact of: (1) stroke severity and timing to inpatient rehabilitation admission on length of stay, functional gains, and discharge destination; and (2) age and sex differences in time to rehabilitation, length of stay in rehabilitation, and discharge destination for stroke patients in Alberta. The first manuscript offers a discussion of post-positivist critical multiplism and its value for nursing research and describes the steps for conducting critical multiplist nursing research using stroke rehabilitation as an example. We identify that post-positivist critical multiplism offers a useful, rigorous approach that relies on a step-by-step method and a collaboration with a team of scholars who offer different perspectives, open questioning and critique, and rigorous attention to minimize biases throughout the research process. We examined a large retrospective cohort to address the research aims. In the second paper, we identified that length of time to rehabilitation admission was not significantly different between stroke severities. Individuals with moderate and severe stroke made significantly larger FIM gains than mild stroke during inpatient rehabilitation. LOS was longer with increasing stroke severity. Time to inpatient rehabilitation admission had small, but significant impacts on functional gains and LOS. Patients with shorter times to rehabilitation admission and those with mild stroke were more likely to be discharged home without needing health services. In the third paper, we identified that mean length of time from acute care admission to inpatient rehabilitation admission was not significantly different between males and females. There was no significant difference in mean FIM change between males and females during inpatient rehabilitation. Mean LOS in rehabilitation was slightly longer among females compared to males. Male patients and those of younger age were more likely than females and those of older age to be discharged home without needing homecare. In conclusion, stroke severity had a significant impact on the conduct of inpatient rehabilitation. The impact of timing on functional gains and LOS was small. Differences in LOS and discharge destination based on sex and age were significant.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".